PyCaret 2.0 on Wine Quality(Imbalance Classes).ipynb

{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Implementing PyCaret 2.0 on Wine Quality Dataset (Highly Imbalance Classes Problem)","metadata":{}},{"cell_type":"markdown","source":"# **Since PyCaret 2.0 is recently launched, i plan to work with it and see if this is as amazing as it is hyped. To be honest i found it wonderful, they way it has reduced the work for data scientists and provide low code model to automate most of the repetition work is marvelous. Let start and see for yourself how it can save precious time for users.**","metadata":{}},{"cell_type":"markdown","source":"# **I've selected Wine Quality dataset due to its highly imbalance classes which is a key problem in dataset for incuring bias.**","metadata":{}},{"cell_type":"code","source":"#important libraries\nimport numpy as np \nimport pandas as pd","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0"},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"**importing wine quality data from UCI ML repo via wget**","metadata":{}},{"cell_type":"markdown","source":"**wget: helps to retrieves content from web servers**","metadata":{}},{"cell_type":"markdown","source":"# 1. Importing Dataset and PyCaret 2.0","metadata":{}},{"cell_type":"code","source":"!wget --no-check-certificate \\\n    https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv","metadata":{},"execution_count":2,"outputs":[{"output_type":"stream","text":"--2020-08-04 05:15:12--  https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\nResolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\nConnecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 84199 (82K) [application/x-httpd-php]\nSaving to: ‘winequality-red.csv’\n\nwinequality-red.csv 100%[===================>]  82.23K  --.-KB/s    in 0.05s   \n\n2020-08-04 05:15:12 (1.56 MB/s) - ‘winequality-red.csv’ saved [84199/84199]\n\n","name":"stdout"}]},{"cell_type":"code","source":"df1 = pd.read_csv('/kaggle/working/winequality-red.csv', sep=\";\")","metadata":{},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"df1.head()","metadata":{},"execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n0            7.4              0.70         0.00             1.9      0.076   \n1            7.8              0.88         0.00             2.6      0.098   \n2            7.8              0.76         0.04             2.3      0.092   \n3           11.2              0.28         0.56             1.9      0.075   \n4            7.4              0.70         0.00             1.9      0.076   \n\n   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n0                 11.0                  34.0   0.9978  3.51       0.56   \n1                 25.0                  67.0   0.9968  3.20       0.68   \n2                 15.0                  54.0   0.9970  3.26       0.65   \n3                 17.0                  60.0   0.9980  3.16       0.58   \n4                 11.0                  34.0   0.9978  3.51       0.56   \n\n   alcohol  quality  \n0      9.4        5  \n1      9.8        5  \n2      9.8        5  \n3      9.8        6  \n4      9.4        5  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fixed acidity</th>\n      <th>volatile acidity</th>\n      <th>citric acid</th>\n      <th>residual sugar</th>\n      <th>chlorides</th>\n      <th>free sulfur dioxide</th>\n      <th>total sulfur dioxide</th>\n      <th>density</th>\n      <th>pH</th>\n      <th>sulphates</th>\n      <th>alcohol</th>\n      <th>quality</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7.4</td>\n      <td>0.70</td>\n      <td>0.00</td>\n      <td>1.9</td>\n      <td>0.076</td>\n      <td>11.0</td>\n      <td>34.0</td>\n      <td>0.9978</td>\n      <td>3.51</td>\n      <td>0.56</td>\n      <td>9.4</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>7.8</td>\n      <td>0.88</td>\n      <td>0.00</td>\n      <td>2.6</td>\n      <td>0.098</td>\n      <td>25.0</td>\n      <td>67.0</td>\n      <td>0.9968</td>\n      <td>3.20</td>\n      <td>0.68</td>\n      <td>9.8</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7.8</td>\n      <td>0.76</td>\n      <td>0.04</td>\n      <td>2.3</td>\n      <td>0.092</td>\n      <td>15.0</td>\n      <td>54.0</td>\n      <td>0.9970</td>\n      <td>3.26</td>\n      <td>0.65</td>\n      <td>9.8</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11.2</td>\n      <td>0.28</td>\n      <td>0.56</td>\n      <td>1.9</td>\n      <td>0.075</td>\n      <td>17.0</td>\n      <td>60.0</td>\n      <td>0.9980</td>\n      <td>3.16</td>\n      <td>0.58</td>\n      <td>9.8</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7.4</td>\n      <td>0.70</td>\n      <td>0.00</td>\n      <td>1.9</td>\n      <td>0.076</td>\n      <td>11.0</td>\n      <td>34.0</td>\n      <td>0.9978</td>\n      <td>3.51</td>\n      <td>0.56</td>\n      <td>9.4</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df1.describe()","metadata":{},"execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"       fixed acidity  volatile acidity  citric acid  residual sugar  \\\ncount    1599.000000       1599.000000  1599.000000     1599.000000   \nmean        8.319637          0.527821     0.270976        2.538806   \nstd         1.741096          0.179060     0.194801        1.409928   \nmin         4.600000          0.120000     0.000000        0.900000   \n25%         7.100000          0.390000     0.090000        1.900000   \n50%         7.900000          0.520000     0.260000        2.200000   \n75%         9.200000          0.640000     0.420000        2.600000   \nmax        15.900000          1.580000     1.000000       15.500000   \n\n         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\ncount  1599.000000          1599.000000           1599.000000  1599.000000   \nmean      0.087467            15.874922             46.467792     0.996747   \nstd       0.047065            10.460157             32.895324     0.001887   \nmin       0.012000             1.000000              6.000000     0.990070   \n25%       0.070000             7.000000             22.000000     0.995600   \n50%       0.079000            14.000000             38.000000     0.996750   \n75%       0.090000            21.000000             62.000000     0.997835   \nmax       0.611000            72.000000            289.000000     1.003690   \n\n                pH    sulphates      alcohol      quality  \ncount  1599.000000  1599.000000  1599.000000  1599.000000  \nmean      3.311113     0.658149    10.422983     5.636023  \nstd       0.154386     0.169507     1.065668     0.807569  \nmin       2.740000     0.330000     8.400000     3.000000  \n25%       3.210000     0.550000     9.500000     5.000000  \n50%       3.310000     0.620000    10.200000     6.000000  \n75%       3.400000     0.730000    11.100000     6.000000  \nmax       4.010000     2.000000    14.900000     8.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fixed acidity</th>\n      <th>volatile acidity</th>\n      <th>citric acid</th>\n      <th>residual sugar</th>\n      <th>chlorides</th>\n      <th>free sulfur dioxide</th>\n      <th>total sulfur dioxide</th>\n      <th>density</th>\n      <th>pH</th>\n      <th>sulphates</th>\n      <th>alcohol</th>\n      <th>quality</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n      <td>1599.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>8.319637</td>\n      <td>0.527821</td>\n      <td>0.270976</td>\n      <td>2.538806</td>\n      <td>0.087467</td>\n      <td>15.874922</td>\n      <td>46.467792</td>\n      <td>0.996747</td>\n      <td>3.311113</td>\n      <td>0.658149</td>\n      <td>10.422983</td>\n      <td>5.636023</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.741096</td>\n      <td>0.179060</td>\n      <td>0.194801</td>\n      <td>1.409928</td>\n      <td>0.047065</td>\n      <td>10.460157</td>\n      <td>32.895324</td>\n      <td>0.001887</td>\n      <td>0.154386</td>\n      <td>0.169507</td>\n      <td>1.065668</td>\n      <td>0.807569</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>4.600000</td>\n      <td>0.120000</td>\n      <td>0.000000</td>\n      <td>0.900000</td>\n      <td>0.012000</td>\n      <td>1.000000</td>\n      <td>6.000000</td>\n      <td>0.990070</td>\n      <td>2.740000</td>\n      <td>0.330000</td>\n      <td>8.400000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>7.100000</td>\n      <td>0.390000</td>\n      <td>0.090000</td>\n      <td>1.900000</td>\n      <td>0.070000</td>\n      <td>7.000000</td>\n      <td>22.000000</td>\n      <td>0.995600</td>\n      <td>3.210000</td>\n      <td>0.550000</td>\n      <td>9.500000</td>\n      <td>5.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>7.900000</td>\n      <td>0.520000</td>\n      <td>0.260000</td>\n      <td>2.200000</td>\n      <td>0.079000</td>\n      <td>14.000000</td>\n      <td>38.000000</td>\n      <td>0.996750</td>\n      <td>3.310000</td>\n      <td>0.620000</td>\n      <td>10.200000</td>\n      <td>6.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>9.200000</td>\n      <td>0.640000</td>\n      <td>0.420000</td>\n      <td>2.600000</td>\n      <td>0.090000</td>\n      <td>21.000000</td>\n      <td>62.000000</td>\n      <td>0.997835</td>\n      <td>3.400000</td>\n      <td>0.730000</td>\n      <td>11.100000</td>\n      <td>6.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>15.900000</td>\n      <td>1.580000</td>\n      <td>1.000000</td>\n      <td>15.500000</td>\n      <td>0.611000</td>\n      <td>72.000000</td>\n      <td>289.000000</td>\n      <td>1.003690</td>\n      <td>4.010000</td>\n      <td>2.000000</td>\n      <td>14.900000</td>\n      <td>8.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"**you can see for yourself how imbalance the classes are, target quality for class 4,8,3 are negligible as compared to 5,6,7. This will surely impact the model performace on minority classes and introduce bias**","metadata":{}},{"cell_type":"code","source":"df1.quality.value_counts()","metadata":{},"execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"5    681\n6    638\n7    199\n4     53\n8     18\n3     10\nName: quality, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"**installing the latest version of PyCaret**","metadata":{}},{"cell_type":"code","source":"pip install pycaret==2.0","metadata":{},"execution_count":7,"outputs":[{"output_type":"stream","text":"Collecting pycaret==2.0\n  Downloading pycaret-2.0-py3-none-any.whl (255 kB)\n\u001b[K     |████████████████████████████████| 255 kB 575 kB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: textblob in /opt/conda/lib/python3.7/site-packages (from pycaret==2.0) (0.15.3)\nRequirement already satisfied: seaborn in /opt/conda/lib/python3.7/site-packages (from pycaret==2.0) 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(1.4.2)\nRequirement already satisfied: defusedxml in /opt/conda/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret==2.0) (0.6.0)\nRequirement already satisfied: mistune<2,>=0.8.1 in /opt/conda/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret==2.0) (0.8.4)\nRequirement already satisfied: webencodings in /opt/conda/lib/python3.7/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret==2.0) (0.5.1)\nBuilding wheels for collected packages: pyod, combo, suod, prometheus-flask-exporter, databricks-cli, sqlalchemy, querystring-parser\n  Building wheel for pyod (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for pyod: filename=pyod-0.8.1-py3-none-any.whl size=105651 sha256=468e9f3ee1062549c477b9f21277cad5c791a13f8c83483e5dfa71ffdf7e1bb9\n  Stored in directory: /root/.cache/pip/wheels/d6/f2/24/2c050361bd259bee34bbef8047bbcd3f08085f2a8a5ed1fc85\n  Building wheel for combo (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for combo: filename=combo-0.1.1-py3-none-any.whl size=42113 sha256=5dfd021545feb373b65924cbb8ce1bb21698bf140f6f6d043d3d845363b4dfc0\n  Stored in directory: /root/.cache/pip/wheels/3e/e1/f8/08f19ba48f75d3dbbb549cec4b86cc0392c14b2b6bb81f4e1f\n  Building wheel for suod (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for suod: filename=suod-0.0.4-py3-none-any.whl size=2167157 sha256=678eb1a2c80c149fdbff47afb8baeba674428dcea668949d4cc4c4d64bbc4279\n  Stored in directory: /root/.cache/pip/wheels/dc/ae/aa/3b8cc857617f3ba6cb9e6b804c79c69d0ed60a08e022e9a4f3\n  Building wheel for prometheus-flask-exporter (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for prometheus-flask-exporter: filename=prometheus_flask_exporter-0.15.4-py3-none-any.whl size=16451 sha256=e5ed543196680fbd7108c543d469249cd160e6255c433b3f9f4f9a9b21d126fc\n  Stored in directory: /root/.cache/pip/wheels/69/e3/ab/f10afded2fa6225f1672b34a7438cadf596e359a21f454d22c\n  Building wheel for databricks-cli (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for databricks-cli: filename=databricks_cli-0.11.0-py3-none-any.whl size=90300 sha256=b5a56f384d753c2d54bc7e8f022672cbe13848119d0b92dcc0f671738a782dca\n  Stored in directory: /root/.cache/pip/wheels/81/3f/18/5678c9d403583e583a251463196998b17852b98de34aa9ab51\n  Building wheel for sqlalchemy (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for sqlalchemy: filename=SQLAlchemy-1.3.13-cp37-cp37m-linux_x86_64.whl size=1221855 sha256=e009c1d130fcb7a3caa35c14649001533f0ec7da0b857267318d6dd4375985aa\n  Stored in directory: /root/.cache/pip/wheels/b9/ba/77/163f10f14bd489351530603e750c195b0ceceed2f3be2b32f1\n  Building wheel for querystring-parser (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for querystring-parser: filename=querystring_parser-1.2.4-py3-none-any.whl size=7076 sha256=5ffc4d11d9964eefab8c52e2f98be00e9b5c1efa9596a3f39c93d30c98919553\n  Stored in directory: /root/.cache/pip/wheels/69/38/7a/072b5863ca334d012821a287fd1d066cea33abdcda3ef2f878\nSuccessfully built pyod combo suod prometheus-flask-exporter databricks-cli sqlalchemy querystring-parser\nInstalling collected packages: combo, suod, pyod, prometheus-flask-exporter, databricks-cli, azure-core, isodate, msrest, azure-storage-blob, sqlalchemy, querystring-parser, gorilla, gunicorn, mlflow, zope.interface, DateTime, datefinder, pycaret\n  Attempting uninstall: sqlalchemy\n    Found existing installation: SQLAlchemy 1.3.16\n    Uninstalling SQLAlchemy-1.3.16:\n      Successfully uninstalled SQLAlchemy-1.3.16\nSuccessfully installed DateTime-4.3 azure-core-1.7.0 azure-storage-blob-12.3.2 combo-0.1.1 databricks-cli-0.11.0 datefinder-0.7.1 gorilla-0.3.0 gunicorn-20.0.4 isodate-0.6.0 mlflow-1.10.0 msrest-0.6.18 prometheus-flask-exporter-0.15.4 pycaret-2.0 pyod-0.8.1 querystring-parser-1.2.4 sqlalchemy-1.3.13 suod-0.0.4 zope.interface-5.1.0\n\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2 is available.\nYou should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\nNote: you may need to restart the kernel to use updated packages.\n","name":"stdout"}]},{"cell_type":"markdown","source":"**Since Problem is classification, you can read about other options here: [Modules](https://pycaret.org/modules/)**","metadata":{}},{"cell_type":"code","source":"from pycaret.classification import *","metadata":{},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"**The use case is simple you have to setup a preprocessing pipeline with 'setup' module, select the target and name your experiment.**\n\n**you can specify a session_id, if you didn't by default a random seed is generated and returned in the Information grid. The unique number is then distributed as a seed in all functions used during the experiment. This can be used for later reproducibility of the entire experiment.**","metadata":{}},{"cell_type":"markdown","source":"# 2. Using Default Setup of PyCaret 2.0","metadata":{}},{"cell_type":"code","source":"session_1 = setup(df1, target = 'quality', session_id=123, log_experiment=False, experiment_name='wine_q1')","metadata":{},"execution_count":9,"outputs":[{"output_type":"stream","text":"Setup Succesfully Completed!\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97f89a9950>","text/html":"<style  type=\"text/css\" >\n</style><table id=\"T_9b17e002_d611_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >session_id</td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >123</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >Target Type</td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >Multiclass</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >Label Encoded</td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >Original Data</td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >(1599, 12)</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >11</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >10</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >(1599, 12)</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >11</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >(1119, 11)</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row12\" class=\"row_heading level0 row12\" >12</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row12_col1\" class=\"data row12 col1\" >(480, 11)</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row13\" class=\"row_heading level0 row13\" >13</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row13_col1\" class=\"data row13 col1\" >mean</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row14\" class=\"row_heading level0 row14\" >14</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row14_col1\" class=\"data row14 col1\" >constant</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row15\" class=\"row_heading level0 row15\" >15</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row15_col0\" class=\"data row15 col0\" >Normalize </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row15_col1\" class=\"data row15 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row16\" class=\"row_heading level0 row16\" >16</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row16_col1\" class=\"data row16 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row17\" class=\"row_heading level0 row17\" >17</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row17_col0\" class=\"data row17 col0\" >Transformation </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row17_col1\" class=\"data row17 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row18\" class=\"row_heading level0 row18\" >18</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row18_col1\" class=\"data row18 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row19\" class=\"row_heading level0 row19\" >19</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row19_col0\" class=\"data row19 col0\" >PCA </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row19_col1\" class=\"data row19 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row20\" class=\"row_heading level0 row20\" >20</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row20_col1\" class=\"data row20 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row21\" class=\"row_heading level0 row21\" >21</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row21_col1\" class=\"data row21 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row22\" class=\"row_heading level0 row22\" >22</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row22_col1\" class=\"data row22 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row23\" class=\"row_heading level0 row23\" >23</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row23_col1\" class=\"data row23 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row24\" class=\"row_heading level0 row24\" >24</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row24_col1\" class=\"data row24 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row25\" class=\"row_heading level0 row25\" >25</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row25_col1\" class=\"data row25 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row26\" class=\"row_heading level0 row26\" >26</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row26_col1\" class=\"data row26 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row27\" class=\"row_heading level0 row27\" >27</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row27_col1\" class=\"data row27 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row28\" class=\"row_heading level0 row28\" >28</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row28_col1\" class=\"data row28 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row29\" class=\"row_heading level0 row29\" >29</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row29_col1\" class=\"data row29 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row30\" class=\"row_heading level0 row30\" >30</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row30_col0\" class=\"data row30 col0\" >Clustering </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row30_col1\" class=\"data row30 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row31\" class=\"row_heading level0 row31\" >31</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row31_col1\" class=\"data row31 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row32\" class=\"row_heading level0 row32\" >32</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row32_col1\" class=\"data row32 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row33\" class=\"row_heading level0 row33\" >33</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row33_col1\" class=\"data row33 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row34\" class=\"row_heading level0 row34\" >34</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row34_col1\" class=\"data row34 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row35\" class=\"row_heading level0 row35\" >35</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row35_col1\" class=\"data row35 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row36\" class=\"row_heading level0 row36\" >36</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row36_col0\" class=\"data row36 col0\" >Group Features </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row36_col1\" class=\"data row36 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row37\" class=\"row_heading level0 row37\" >37</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row37_col1\" class=\"data row37 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row38\" class=\"row_heading level0 row38\" >38</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row38_col1\" class=\"data row38 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row39\" class=\"row_heading level0 row39\" >39</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row39_col1\" class=\"data row39 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row40\" class=\"row_heading level0 row40\" >40</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row40_col1\" class=\"data row40 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row41\" class=\"row_heading level0 row41\" >41</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row41_col1\" class=\"data row41 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row42\" class=\"row_heading level0 row42\" >42</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row42_col0\" class=\"data row42 col0\" >Fix Imbalance</td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row42_col1\" class=\"data row42 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9b17e002_d611_11ea_8583_0242ac130202level0_row43\" class=\"row_heading level0 row43\" >43</th>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row43_col0\" class=\"data row43 col0\" >Fix Imbalance Method</td>\n                        <td id=\"T_9b17e002_d611_11ea_8583_0242ac130202row43_col1\" class=\"data row43 col1\" >SMOTE</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"**you can complete details about all functions here [1](https://pycaret.org/classification/)**","metadata":{}},{"cell_type":"markdown","source":"**compare_model(): This function train all the models available in the model library and scores them.**\n\n**I blacklisted 'catboost' as it was taking much time in k-folds, you can test if it helps in accuracy and other metrics**","metadata":{}},{"cell_type":"code","source":"best_model = compare_models(blacklist=['catboost'])","metadata":{},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e2be8410>","text/html":"<style  type=\"text/css\" >\n    #T_e93961ac_d611_11ea_8583_0242ac130202 th {\n          text-align: left;\n    }    #T_e93961ac_d611_11ea_8583_0242ac130202row0_col0 {\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row0_col1 {\n            background-color:  yellow;\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row0_col2 {\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row0_col3 {\n            : ;\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row0_col4 {\n            background-color:  yellow;\n            text-align:  left;\n        }    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text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row13_col1 {\n            : ;\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row13_col2 {\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row13_col3 {\n            : ;\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row13_col4 {\n            : ;\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row13_col5 {\n            : ;\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row13_col6 {\n            : ;\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row13_col7 {\n            : ;\n            text-align:  left;\n        }    #T_e93961ac_d611_11ea_8583_0242ac130202row13_col8 {\n            background-color:  lightgrey;\n            text-align:  left;\n        }</style><table id=\"T_e93961ac_d611_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>        <th class=\"col_heading level0 col7\" >MCC</th>        <th class=\"col_heading level0 col8\" >TT (Sec)</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >Extra Trees Classifier</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.6658</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.3701</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6409</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.6453</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4541</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row0_col7\" class=\"data row0 col7\" >0.4584</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row0_col8\" class=\"data row0 col8\" >1.3902</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.6524</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.3760</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6256</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.6344</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4388</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row1_col7\" class=\"data row1 col7\" >0.4432</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row1_col8\" class=\"data row1 col8\" >0.5277</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >Extreme Gradient Boosting</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.6435</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.3752</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6178</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.6274</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4278</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row2_col7\" class=\"data row2 col7\" >0.4306</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row2_col8\" class=\"data row2 col8\" >0.6911</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >Random Forest Classifier</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.6426</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.3728</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6176</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.6267</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4265</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row3_col7\" class=\"data row3 col7\" >0.4296</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row3_col8\" class=\"data row3 col8\" >0.3988</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >Gradient Boosting Classifier</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.6283</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.3589</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.6082</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.6130</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.4013</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row4_col7\" class=\"data row4 col7\" >0.4043</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row4_col8\" class=\"data row4 col8\" >1.6138</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >Ada Boost Classifier</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.6024</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.3498</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.5848</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.5854</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.3544</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row5_col7\" class=\"data row5 col7\" >0.3586</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row5_col8\" class=\"data row5 col8\" >0.9083</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >Linear Discriminant Analysis</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.5969</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.2892</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.5741</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.5714</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.3352</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row6_col7\" class=\"data row6 col7\" >0.3410</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row6_col8\" class=\"data row6 col8\" >0.0364</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >Ridge Classifier</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.5880</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.2509</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.4973</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.5318</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.2989</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row7_col7\" class=\"data row7 col7\" >0.3103</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row7_col8\" class=\"data row7 col8\" >0.0383</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >Logistic Regression</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.5853</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.2602</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.5387</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.5411</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.3005</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row8_col7\" class=\"data row8 col7\" >0.3098</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row8_col8\" class=\"data row8 col8\" >0.2686</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >Naive Bayes</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.5657</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.3083</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.5614</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5588</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.3155</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row9_col7\" class=\"data row9 col7\" >0.3183</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row9_col8\" class=\"data row9 col8\" >0.0245</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >10</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >Quadratic Discriminant Analysis</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.5594</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.3334</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.5543</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.5482</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.2936</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row10_col7\" class=\"data row10 col7\" >0.2994</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row10_col8\" class=\"data row10 col8\" >0.0293</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >11</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >Decision Tree Classifier</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.5121</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.3824</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.6239</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.5551</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.3108</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row11_col7\" class=\"data row11 col7\" >0.3236</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row11_col8\" class=\"data row11 col8\" >0.0574</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row12\" class=\"row_heading level0 row12\" >12</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row12_col0\" class=\"data row12 col0\" >K Neighbors Classifier</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row12_col1\" class=\"data row12 col1\" >0.4835</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row12_col2\" class=\"data row12 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row12_col3\" class=\"data row12 col3\" >0.2604</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row12_col4\" class=\"data row12 col4\" >0.4667</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row12_col5\" class=\"data row12 col5\" >0.4728</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row12_col6\" class=\"data row12 col6\" >0.1828</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row12_col7\" class=\"data row12 col7\" >0.1840</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row12_col8\" class=\"data row12 col8\" >0.0279</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e93961ac_d611_11ea_8583_0242ac130202level0_row13\" class=\"row_heading level0 row13\" >13</th>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row13_col0\" class=\"data row13 col0\" >SVM - Linear Kernel</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row13_col1\" class=\"data row13 col1\" >0.4432</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row13_col2\" class=\"data row13 col2\" >0.0000</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row13_col3\" class=\"data row13 col3\" >0.2087</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row13_col4\" class=\"data row13 col4\" >0.3790</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row13_col5\" class=\"data row13 col5\" >0.3354</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row13_col6\" class=\"data row13 col6\" >0.1040</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row13_col7\" class=\"data row13 col7\" >0.1401</td>\n                        <td id=\"T_e93961ac_d611_11ea_8583_0242ac130202row13_col8\" class=\"data row13 col8\" >0.0492</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"**models() provide list of models in library and their id that can be used in functions**","metadata":{}},{"cell_type":"code","source":"models()","metadata":{},"execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"                                     Name  \\\nID                                          \nlr                    Logistic Regression   \nknn                K Neighbors Classifier   \nnb                            Naive Bayes   \ndt               Decision Tree Classifier   \nsvm                   SVM - Linear Kernel   \nrbfsvm                SVM - Radial Kernel   \ngpc           Gaussian Process Classifier   \nmlp                        MLP Classifier   \nridge                    Ridge Classifier   \nrf               Random Forest Classifier   \nqda       Quadratic Discriminant Analysis   \nada                  Ada Boost Classifier   \ngbc          Gradient Boosting Classifier   \nlda          Linear Discriminant Analysis   \net                 Extra Trees Classifier   \nxgboost         Extreme Gradient Boosting   \nlightgbm  Light Gradient Boosting Machine   \ncatboost              CatBoost Classifier   \n\n                                            Reference  Turbo  \nID                                                            \nlr            sklearn.linear_model.LogisticRegression   True  \nknn            sklearn.neighbors.KNeighborsClassifier   True  \nnb                     sklearn.naive_bayes.GaussianNB   True  \ndt                sklearn.tree.DecisionTreeClassifier   True  \nsvm                sklearn.linear_model.SGDClassifier   True  \nrbfsvm                                sklearn.svm.SVC  False  \ngpc                      sklearn.gaussian_process.GPC  False  \nmlp              sklearn.neural_network.MLPClassifier  False  \nridge            sklearn.linear_model.RidgeClassifier   True  \nrf            sklearn.ensemble.RandomForestClassifier   True  \nqda                 sklearn.discriminant_analysis.QDA   True  \nada               sklearn.ensemble.AdaBoostClassifier   True  \ngbc       sklearn.ensemble.GradientBoostingClassifier   True  \nlda                 sklearn.discriminant_analysis.LDA   True  \net              sklearn.ensemble.ExtraTreesClassifier   True  \nxgboost                        xgboost.readthedocs.io   True  \nlightgbm                github.com/microsoft/LightGBM   True  \ncatboost                                  catboost.ai   True  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Name</th>\n      <th>Reference</th>\n      <th>Turbo</th>\n    </tr>\n    <tr>\n      <th>ID</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>lr</th>\n      <td>Logistic Regression</td>\n      <td>sklearn.linear_model.LogisticRegression</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>knn</th>\n      <td>K Neighbors Classifier</td>\n      <td>sklearn.neighbors.KNeighborsClassifier</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>nb</th>\n      <td>Naive Bayes</td>\n      <td>sklearn.naive_bayes.GaussianNB</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>dt</th>\n      <td>Decision Tree Classifier</td>\n      <td>sklearn.tree.DecisionTreeClassifier</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>svm</th>\n      <td>SVM - Linear Kernel</td>\n      <td>sklearn.linear_model.SGDClassifier</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>rbfsvm</th>\n      <td>SVM - Radial Kernel</td>\n      <td>sklearn.svm.SVC</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>gpc</th>\n      <td>Gaussian Process Classifier</td>\n      <td>sklearn.gaussian_process.GPC</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>mlp</th>\n      <td>MLP Classifier</td>\n      <td>sklearn.neural_network.MLPClassifier</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>ridge</th>\n      <td>Ridge Classifier</td>\n      <td>sklearn.linear_model.RidgeClassifier</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>rf</th>\n      <td>Random Forest Classifier</td>\n      <td>sklearn.ensemble.RandomForestClassifier</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>qda</th>\n      <td>Quadratic Discriminant Analysis</td>\n      <td>sklearn.discriminant_analysis.QDA</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>ada</th>\n      <td>Ada Boost Classifier</td>\n      <td>sklearn.ensemble.AdaBoostClassifier</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>gbc</th>\n      <td>Gradient Boosting Classifier</td>\n      <td>sklearn.ensemble.GradientBoostingClassifier</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>lda</th>\n      <td>Linear Discriminant Analysis</td>\n      <td>sklearn.discriminant_analysis.LDA</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>et</th>\n      <td>Extra Trees Classifier</td>\n      <td>sklearn.ensemble.ExtraTreesClassifier</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>xgboost</th>\n      <td>Extreme Gradient Boosting</td>\n      <td>xgboost.readthedocs.io</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>lightgbm</th>\n      <td>Light Gradient Boosting Machine</td>\n      <td>github.com/microsoft/LightGBM</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>catboost</th>\n      <td>CatBoost Classifier</td>\n      <td>catboost.ai</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# **creating 4 best model from default setup output and tuning them to see improvements**","metadata":{}},{"cell_type":"code","source":"#creating random forest model\nrf = create_model('rf')","metadata":{},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e3f42990>","text/html":"<style  type=\"text/css\" >\n    #T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.7232</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.4492</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.7043</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.7069</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.5540</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.5629</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6518</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3929</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6264</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6385</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4395</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4404</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.5893</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3009</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.5809</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.5831</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.3561</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.3574</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6607</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.4807</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6330</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6460</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.4586</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4598</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.6696</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.3212</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.6307</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.6468</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.4625</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.4658</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6250</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3118</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6057</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6111</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4059</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4102</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.5625</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2925</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.5310</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.5442</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.3007</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.3030</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6875</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3355</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6467</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6644</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.4926</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.4959</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.5982</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.4472</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.5853</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.5826</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.3455</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.3490</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6577</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.3965</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6319</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6437</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.4500</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.4517</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6426</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3728</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6176</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6267</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4265</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4296</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0463</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0659</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0437</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0447</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0717</td>\n                        <td id=\"T_f2ef2b96_d611_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0729</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"#creating Extra Trees Classifier\net = create_model('et')","metadata":{},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e96c6cd0>","text/html":"<style  type=\"text/css\" >\n    #T_017733c0_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_017733c0_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_017733c0_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_017733c0_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_017733c0_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_017733c0_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_017733c0_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_017733c0_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6696</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.4026</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6524</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6572</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.4625</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4652</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6339</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3446</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6266</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6126</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.3912</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.3970</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6607</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3298</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6364</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6465</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4539</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4567</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6696</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.4674</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6287</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6435</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.4610</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4654</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.6607</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.3010</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.6230</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.6329</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.4415</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.4480</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.7143</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3479</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6794</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6960</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.5434</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.5449</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6518</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.3291</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6166</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6302</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.4338</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.4381</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.7054</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3263</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6663</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6789</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.5182</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.5234</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.5982</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.4387</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6130</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.5775</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.3343</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.3413</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6937</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.4139</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6670</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6777</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5015</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5044</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6658</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3701</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6409</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6453</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4541</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4584</td>\n            </tr>\n            <tr>\n                        <th id=\"T_017733c0_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0325</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0532</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0223</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0331</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0577</td>\n                        <td id=\"T_017733c0_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0565</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"#light GBM Model\nlgbm = create_model('lightgbm')","metadata":{},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e32f2c10>","text/html":"<style  type=\"text/css\" >\n    #T_0599882c_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_0599882c_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_0599882c_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_0599882c_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_0599882c_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_0599882c_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_0599882c_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_0599882c_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6607</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3987</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6495</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6495</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.4539</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4600</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6518</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3926</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6205</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6342</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4335</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4358</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6161</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3028</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.5970</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6013</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.3767</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.3826</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6786</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.4711</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6403</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6510</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.4733</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4789</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.6429</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.3269</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.6126</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.6253</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.4216</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.4241</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6250</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3194</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6079</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6151</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4132</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4144</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6518</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.3451</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6168</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6269</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.4391</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.4479</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6786</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3143</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6328</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6508</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.4797</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.4851</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6339</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.4695</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6182</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6191</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.4057</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4088</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6847</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.4199</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6603</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6704</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.4917</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.4947</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6524</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3760</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6256</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6344</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4388</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4432</td>\n            </tr>\n            <tr>\n                        <th id=\"T_0599882c_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0223</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0602</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0187</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0198</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0344</td>\n                        <td id=\"T_0599882c_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0348</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"#creating XGB model\nxgboost = create_model('xgboost')","metadata":{},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97f95f42d0>","text/html":"<style  type=\"text/css\" >\n    #T_09c19494_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_09c19494_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_09c19494_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_09c19494_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_09c19494_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_09c19494_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_09c19494_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_09c19494_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6429</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3991</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6155</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6287</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.4250</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4260</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6518</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3923</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6241</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6357</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4387</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4413</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6161</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3122</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6053</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6068</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.3888</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.3925</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6786</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.4791</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6402</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6541</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.4774</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4821</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.6161</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.3158</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5883</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.6016</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3863</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3873</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6161</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3158</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.5950</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6045</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.3987</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.3994</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6696</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.3526</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6317</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6464</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.4687</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.4742</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6875</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3268</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6534</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6684</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.4997</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.5020</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.5536</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.4292</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.5469</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.5408</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.2736</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.2766</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.7027</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.4290</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6779</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6875</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5210</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5249</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6435</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3752</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6178</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6274</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4278</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4306</td>\n            </tr>\n            <tr>\n                        <th id=\"T_09c19494_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0422</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0560</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0349</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0394</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0678</td>\n                        <td id=\"T_09c19494_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0683</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"**You can witness how easy is to create model and CV it**\n\n**Similarly, you can tune your model with best hyper params with just single line of code, isn't it amazing**","metadata":{}},{"cell_type":"code","source":"#Hyper params tuning via tune_model\ntuned_rf = tune_model(rf)","metadata":{},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e9cb5750>","text/html":"<style  type=\"text/css\" >\n    #T_1aaac096_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_1aaac096_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_1aaac096_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_1aaac096_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_1aaac096_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_1aaac096_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_1aaac096_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_1aaac096_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6964</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.4258</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6772</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6818</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.5061</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.5117</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6875</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3904</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6612</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6670</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4837</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4882</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6339</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3100</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6103</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6176</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4055</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4109</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.7054</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.4821</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6644</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6765</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.5173</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.5233</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.6429</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.3187</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.6198</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.6275</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.4226</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.4257</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6786</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3329</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6402</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6582</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4852</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4872</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6161</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.3227</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.5829</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.5955</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.3778</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.3821</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.7054</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3259</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6555</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6757</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.5190</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.5246</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.5893</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.4346</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.5618</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.5676</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.3264</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.3302</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.7117</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.4425</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6824</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6961</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5345</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5363</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6667</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3786</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6356</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6464</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4578</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4620</td>\n            </tr>\n            <tr>\n                        <th id=\"T_1aaac096_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0408</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0605</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0387</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0401</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0668</td>\n                        <td id=\"T_1aaac096_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0668</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"**And you can see the params of your best model as well**","metadata":{}},{"cell_type":"code","source":"tuned_rf","metadata":{},"execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"OneVsRestClassifier(estimator=RandomForestClassifier(bootstrap=False,\n                                                     ccp_alpha=0.0,\n                                                     class_weight=None,\n                                                     criterion='entropy',\n                                                     max_depth=20,\n                                                     max_features='log2',\n                                                     max_leaf_nodes=None,\n                                                     max_samples=None,\n                                                     min_impurity_decrease=0.0,\n                                                     min_impurity_split=None,\n                                                     min_samples_leaf=4,\n                                                     min_samples_split=7,\n                                                     min_weight_fraction_leaf=0.0,\n                                                     n_estimators=60, n_jobs=-1,\n                                                     oob_score=False,\n                                                     random_state=123,\n                                                     verbose=0,\n                                                     warm_start=False),\n                    n_jobs=None)"},"metadata":{}}]},{"cell_type":"code","source":"tuned_et = tune_model(et)","metadata":{},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e2bd13d0>","text/html":"<style  type=\"text/css\" >\n    #T_30ea3a76_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_30ea3a76_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_30ea3a76_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_30ea3a76_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_30ea3a76_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_30ea3a76_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_30ea3a76_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_30ea3a76_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.7054</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.4297</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6798</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6898</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.5199</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.5228</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6607</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3768</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6339</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6389</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4384</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4438</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6696</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3239</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6391</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6500</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4579</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4622</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6964</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.4782</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6620</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6663</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.5003</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.5080</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.6429</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.3020</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.6162</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.6195</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.4120</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.4176</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.7054</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3440</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6658</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6849</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.5283</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.5300</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6339</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.3300</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6009</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6126</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.4064</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.4119</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6964</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3225</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6521</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6664</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.5005</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.5071</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.5357</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.4041</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.5580</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.5117</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.2248</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.2302</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.7027</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.4087</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6977</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6863</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5084</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5160</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6649</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3720</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6405</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6426</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4497</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4550</td>\n            </tr>\n            <tr>\n                        <th id=\"T_30ea3a76_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0499</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0541</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0388</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0507</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0859</td>\n                        <td id=\"T_30ea3a76_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0857</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Plotting ROC for initial/basic model**","metadata":{}},{"cell_type":"code","source":"plot_model(rf)","metadata":{},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Plotting ROC for hyper parameters tuned model**","metadata":{}},{"cell_type":"code","source":"plot_model(tuned_rf)","metadata":{},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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FK+jo6Ojo3Ma0avCvnv3bmbMmMHf/va3Dvs+/fRTrrnmGr71rW9pa1I6Ojo6Ojo6J0evCXsgEODBBx88alKPhx56iN///ve89NJLfPzxxx2ScOjo6Ojo6Oh0n14TdrPZzLPPPktWVlaHfaWlpaSkpJCbm4ssy1xyySV89tlnvdUVHR0dHR2dM4Ze84o3Go0dUlW2Ultbm5TxyuPxaOErnaGqKn6/H5PJdNzQHx0dHR0dndMBIYQW/XFkuuNj0Sfhbp054h9LsP1+P7t37+7NLuno6Ojo6PRLhg8fjsvl6nL7PhH27OxsLfkHxDM8ZWZmHrW9yWQC4m/uWKk1dU6OkpISxo4d29fdOO3R7/Px+aqumcZwck34oSl2chxWALbVNuGLKh2OS7eaGJXuoqSkBFfREMp8oQ5tZAnOz41bDBvDUUq8zahCoIr4pEMAqhBMzk4jzRp/9qw6WE1M7dhmZLqL0RnxB+5HZXWU+YKoQiAS7VQEHpuFK4bkAFDibWZ9RX2n7/k7Y4owG2Qaw1Fe3lXeaZuZRZkMTXUA8PcdpbR0cg9Gpjm5tDCeefCtPz1P5fkdCyVZ6r1M/WU821/tuMls//b/1+n1pjx6J/aaKhSzhfUP/7nTNkPf+BsF698H4Mtb/puWwsFIqoKkqkiqihKRMX9ZiudPKwHwzZyA7/IJoAgkVQVFRVJUiClkPRxPzhQbnEPTtZfE96sCSYmfCwGp67ZjqmkGg0LDFRPAJJDlGLIcRZJjyIYYjqaDuJv3IlujtOQOIep0IZtiyMZI/McUxWRsIMOwDdkaIWxII0g+EiqgIBNDQiChYOcwMioqMhHSkVBQMREmg7CUSUTOJUIOYTkTAyqjjW8RFSbKlMnsUq/seMOEgCMmsooSJRQO4m1sYu2DSxFqjFffeIvc9FR2796taWBX6RNhLygowOfzUVZWRk5ODuvWreOxxx47avvW2bzZbMZisZyqbp6R6Pf31KDf52RaIjFUIUixmPisop6Pyjum6U1z2ChO3LedzSEq/eEObc5KdTAxN96mKqzyudfXoY1Rlrh0UC4AkYjKpjp/p30an2vQ/k97WyLE2lkaZQlkJIqEpLWJyDIBIcf3yRKyBAYkTO2eWy67DZe3GqWmOi5+SqsAKuy+9SqM4RBhVwoZ3/pufLuiJLWr+3QN0cP7AfDMWECq1Z7Yr2piaio/xM6dXwKQ4UzFveXfDPrtk8hSvE+yJGGSiyj8KF69LxhTGBuKIiX2tbaRkXD98w0MsoQQglExtW2fBFu3bOWcyZOQpjwIPAjAxHb3b+vP/s7h1zYQKKvHXpDOwkPPI4SKqvhQlebETwuq0oyiBOJ/L5zUbvs78b/VFpRYYpvagnpJ/G+hBukKHUv+SMgGN7LBlfidhcvgxmBwIhtcCDmVINkE8eAXGdSqbnyKg2ybxNh0iVBMsKbSwn6fLfm0KkgiSE3YT1TyEJFyicoxkJIno1ElSkV9Mwe9AUrrAjgMMhcPzmTB2CLOvzCTFdyDw+FgeHEh4XD8M97dJeheS1BTUlLCo48+Snl5OUajkezsbKZPn05BQQEzZ87k888/18R81qxZR80CBhAOh7VZjv5A7D02b97M5MmT+7obpz2n+j5HVZWY2vFrbpQkTIb4ul1YUVE7eRTIgMVoACCmqkQ7OQ+A1SAjSRKqEIQVtdM2JlnCmFgnDMUUROK6/yr1sqvBx0X5GVyQl85X3mY2VDVwVqqTEelO7XiXyYjdFO9LfSjSaV8sBplUi4nNmzczYtwE/LGOM1oJyLInxFhRaQpHkSWpTdiI/7YZDRjk+AM1qqiaoHXnIVt6z8+of+P1pG2Rw/FUtuai4i6fp1MsKhiP//hOm7+A/HsfOG674yHUMEo7Md6z+wsGD8ri0OtraNqxG9keQbZFka3x30LyY7BHMaYKzGlgcMRQ1Y4Dra4iyQ4MBnc7UXYdIdDOI/YntzMkXkty3OLhiyrUhyLUhSIUOG3aZ+K5/xzEG4p2uL6DSlyRVQihEpSHEJHjKZdVTCA7UCQ3Csn6ZACa/GFKyuvZU91Meb2fUCTGpcNyuGJ0PnNH5vHGC8+yatUqVq5c2WFmfqLa12sz9rFjx7Js2bKj7p8yZUpSwQgdHZ2jE1FUvqprJqoKMm1mBqfEH04HmvzUBiMd2psNMhMzU6gLRvjrV4eTZputTM1J45KEyXblgWp2N3R86GbbLXx7TLyyWom3hdWHOq+m96OJQ7CbDLREYvzxy4OdtlkwJJvRGW4Alu0opf6Ih+euBh8X5KUzxuNmjMd9lDsRJ916/CU5p9mI03zsR5zZIJNpP/4Ds3UA1F3q33idSHkZ5vy2vPvmomLSr7yawl/++oTOCeBrWEnZrq8DnQ+i2hPgGfZseuaEr3U0rEDlPjBPhMyJnbcRigmjNQ1ZThbaNgF2Jn672wlw56ItSYYu9y2mqrREYjRHYuRYLViMBhRV8NreCprDdTRHokkDwyF2L+nSbpqDdQSjg7BixSD8yMKHjB9JKMiGFBTrJcTkDMKqk7Ca3B+nyYDTaKC2KcimQ7V8sKOChkD8u5njsjF/dD7zZo5nxvBcnJY2AT9w4AAVFRUcOHBAK0d8sui54nV0+glCCLzBCBaDjNtiYkt1I1trmwDwthPvCZluTdh3NfjYVtvc4Vxus5GJmSlYDDKXFnr4j7eZlCNEzmNrE8c8hzW+9ncEKe0eQCkWI8MT67tH0qp7Rlk6ahuXue1cg9x2PNb4GrokSVyQl06mbeD5z3Q2I29Pq6hP2L6vR68bCe0GVKzOczGa8nr03EdDkkzIxhRtZrzrlc1Ev/IRLI9gsqdxwYt3xkVYdmEwxn9LcvfWhruCEAJfVMEXiZHrjPtc1IcifFjqpTkh5oF2lprZBTJ2qZ7mkJfDTYMQqMjCh0VtwiiaMIhGmhpq8NOC0WAlz1qPZMonKg0ioDppihoJtZ5Ojf+4TEaK3BY8NjO1TUE+21/N/5aUsdfbol13SmEG8y8qYN6ofM7OT0dOWH9isRhr1qzh8ssvB+BXv/oVqqomlXs+WXRh19HpBXbVt3CopeMaoFmWafVt9QbDbKlp0vaVeOMz8rEZLuYPycFpNmKQJJojUawGmYiqckmBh0Fuu3bMpKxUhnUipEYprrROs5HJ2alMzj72Q+O83DQg7ZhtBqc4tAHF0XCYjFx51vGFZmZxx/wWPc3xRLcnOJ5Z3ZxfQPqVV/fa9TPy7sKV/rVeO/+xWPv491FqfNgL0sm5Zip217QeOW9EUQnGFG1Qeag5wJe1zTRHorREYrREY7ROtv9r0hBUNUidz8vuxigyKiYpgl3ygdKEpDazaf9ejCI++M1gPSbZgNuWgcuaidmSTUwaTkB10hg1UhuMUR5ToZ3fpttspMhlIdthJcdhwaAK1u2p5qVte3h/VyUt4bjlyWkxcuW4IuaNymfeqHxy3EeswSf40Y9+xCuvvMJbb73FRRddhNt9bOvUiaALu47OcVCF4GBzAKvBQJ7TSllLkOX7q/DHFI5cbb36rDyK3XbKfCG2thPtVmxGA62Pv+ZIrNM2rWbm4WlOhqc5O+xvT5bdoq0N6iTTmRm8p+kJs/pAxl6QzsL9vz/h4yv9ISp8IW2tuz4YpSUaI8tuYXFiCag5EmN7fXwmbDOouAwRjARAbeKVjf9EUVoQSGRiQSKEBBhkE25rRlzAbWfjsniQjOkEhYuGsERVIMwBf5iQr3UpQwUipFiMFLnsZNst5DgsZNut2IwyX5Q3sOKrMlZuL2djqVczbg3JcPLtc4cyf1Q+Fw/N1vxRjsUtt9yCJEm9GhmjC7uOzlEIKyoxVWVjVQMbqxoZ5LbzrRH5OEwG3BYTTZEYOUeIqinhHHZebhrjMzuOxGUkDm5vACDfaeM7Y4uS9lsNBlzHWRc+E1Fi9Qi1o0PTURF14FYwu3MZ89GnvdexBLFIda9foz2q0nL8RidBq0f7sYjVtGAp6Ohz3krr0tLhliBN4ahmJi9227ikIO7bsa22KWkpyWGUyLIqOKjl412f0RyqozHYhCemxNe6aR+ZYMBpzcBtK8Bt8+C2ehIzcQ9hYaU6EKU6EGKvP0xVfTjh1Nk2kE61mBjktmsCnu2wYEsIsz8cZc2eKh7ZERfziua49c0gS1wyJJt5o/KZP7qAEVnu4zpT7t69m/vuu48nn3ySrKwsJk2axNNPP33MY04W/Qmic0ZT5Q9xsDmgvR6b4cZpNlLtD/HX7cnZEG3GuGinWc3MKMrEYzMjH+VL7TQZcZo6/3odTPy2GGQybfps+3g01rxA1f6jR810hgPgifjfe7fk93if+g29lImzfZja0TBmuSi6ZiqqEDSFo9SFIqRaTHgSn+mXdpVTesRylCyByxjjoLeS5qAXNRCg0BghGqlAiVYiJ3IXBIB9gCTJuCzpZLty4uKdEHCXLQOHJRUJifpQXMDL/WE2N4WpDtQROSIyI91qYkiKnWy7NSHkFqxHzK4P1vtYub2cFTvKWLe3inAsfo4Mu4UbJg9m/qgCZo/MI7WbviDr1q3jvffe44033uD73/9+t449UXRh1zkjaQ5H+fvOMpojyUlQil12nGZj0qx5ZJoTm8nA5YVtSZR08/epIxLaBYDdfRkGo6fTNo2rVqAGg8i2+LqmqqrIsow5vwD7uAmnrK+nEoMxFbvrYqBrM+zuoMWeH8PMvnbTFxxIyeJfW/YRSSx6n5+bzgV5Mi3BehyyjzxrGKdUh6rUEA5XEA7X0BSCf7ULrpCQcFrTcDmG4LZlJM2+ndY05IQ3vCoE9aEI1f4wO2rCVPnLqQmEtWu3km41kZPiINthIcduIdthwWLoaCKPKSobDnlZsb2MlTvKKalqy50wLjeV+aMLmD8qn/OKPRi6kc4V4ODBgxQVFSHLMrfccgujRo3i4osv7tY5TgZd2HXOSEKKqol6utXE9IRot2Yas5uMXD+ygHyn9aizcp2e4XhOboarGzDOh8Y7dyD2dT6gipSHMecXMS7hfd7buQJ6WkhPnHsB8B+KZ/J0FHc+8OkutsJ0sq+/iEpfiJZo3ITusZoZlBJ33Fx5oJL/CAc0+rEbYqSafRjUBg5VrmX/wYPQzmQeN35LOCwpZKQMayfeGbhsHlzWdAxyshSpQlAXjHCozk+1P0RVIExNIJwUoiYR90fJ0QTcSpbdguUYoYkNgTCrdlawckc5q3aWU58IR7MaDcwdlc/80fnMH1VAUdqxnUSPxerVq1m8eDE///nP+eEPf4gsy6dU1EEXdp0zgIiisr/Jz1v7qrAZZS7O9zA+082FeelMyk7V1tWOpNDVuVerTvc5lnj3RMKW3vY+P5KumKpPJY5iD0XXTOXsX9/Q5WOEEARi8bAxq9GgeaFvqKxnU3Uju6IK7Ghbjsq1NLBbLqEl5KUyOhiTnItD2YYlHG+jAnazG5d7UJvZvNV0bk3HaOg89E1RBTWBMFX+ENWBMFX+MDXBcFJSJQnIsJnJsVvIcVjJTjiNmo+TX0AIwY7qJlZsL2fljjI+OViLkjhvfoqdW88vZv7oAqYPy8HeQ74tkyZNori4mMLCwh4534mgC7vOgEdRRVLcqlGWNLFefbCGL2rbHGaCMRVvMIIsSUzLP7rjT4/3MRZPjQkgiRqikc5zgfc3Kh59iMZ3V570eaLl8fdryu+43m1OzSd17jzy7ry302Pryh+lsfppzvrHq9hc5590X06U9rP0rpiq+5pW5zWn2ah9H97ZV0VTImzM1y5sbGwaDHV4aQ56+bLRg1/xYBSNmJUyZPwYhJ9IpIEK0YjV5GSIo46ov47BhaNx2y7WZt8mw7HXnxVV4A2GqQqEqfaHqQqEqA10TNfrsZrj4WUJU3qWzdLlJEHhmMKH+6pZsb2MFdvLOVAfT7wkSXBekSduYh+dz/jctB6pFqqqKn/+85+ZMmUKkydPJjMzk08++aRb1dh6Gl3YdfqE+lCEnfUdM52dk52K2SATVhQ2V3cMBQMYlurQ1ri31jTy3qHapP3FbhuTs1I5K81Jpb+tCMiF+emMy3DjtvR80oxjEQkd4MC2MQgRN/vZgX1bTmkXTpyZYJl58qdpM6B3PqAJ8iz7tjx7nLP03YMSkmfp9oJ0iq6Z2qf9aU8oprCttpmWaAxfJKm5DBsAACAASURBVEZLJEZDOEowpnDlsFzy7QrNQS8HmqIEFRmTFMEsAqA2I6k+ymrKqVXjM28jFgqMZlJtaYmQsVxctviat9vqwWyMJ4XZvHkz4wuPvtwRU+OD6Cp/mOpAiCp/mNpgBOUIEc+0WZLCy7LsZi31cFepbA6wckc5K7aX88HuSvyJZTa31cQ1E4qZPzqfuSPzyUwktOlJvvjiC5YuXcoFF1zA8uXL4++rD0UddGHX6QOEEDz7n0Od7huf6U4Iu8rH5XWdtnGbjZqwtxf/0enx1C+yFA9lAbhpVKG2rSdG5ydCLFKKEBHMtjFY7eOpr68nPb1/mHCPhX/bVkK7diLb7aRdsbBP+2IwZWJ1TOrTPsDJx233FPub/HxSXs81w/OwJdKl/qusrWKmhMAshXFLXtbveBtZjQ9+nVhwEUFCYDJYE+byDNzW4bhtF2ivLUb70S7dKTFVpTYQoSoh4NWBMLXBMO392gySRKbNnHBqi4eXZdq6L+IAqirYUl6fmJWXsbmsrWLe8Ex3fK18dAEXDs464XTAx76+SjgcxmazMWnSJJ566ilmzuyBEXAPoQu7zinhy9omynwhsu0Wxme6Gedx8x9vM98cnofULs2LNfEltBsNfGt452FK7VOhzh2cRVQRZNnN2DsJL2st4tEfcKV/nczCB6jcvJm8s/pnsZ32a+Gta985P/4BeWedmQlYWtn6s7/jP+TtMee0YyEShXRMsqxVVtta24QvEqMxHKIxFKIyIADBml3LUSOHaA7VkSKyE/nNA8gEkQCjbE44q43XZtxt4u04ocFuNCHipcJE9YFqqv0hvKFIBxHPtlu08LIcuwWPzXJS38eWUJQP9lRqXuzVLXFrnFGWuPysHOaPjqdvPauT/BE9idfrZfHixRQXF/PUU08BcP311/fqNbuLLuw6vc7B5gDvHozHt4RSHUzOTmXe4GzmDc4+6jFGWdY8cI9FvlN3cOtJ2mdrG+hZ1VShUFa/k0isY0327rLvlY8AcM0dxN7qzSd0DkVAICYTUiQyrXGfEF9UYmezhZAiEVZkwqpEWJFQkbg4y4cVLy2hOjY0T0C0e1wb1AZSYh/iDddpWdbybS5ctsFaqJjb5sFmcp2UpSqqqFQH4jPwqkCIan8YbzCS8Hm3gbcZoySR0y4+PMdhJcNq7pFB9T5vCyt3lLF8ezkf7qsmmohPz3RauHnKUOaNymfm8FxSTmGdgZSUFHw+H01NTUSj0W7XSj8V6MKu0+MoqtC+1OtKa9mYiA9NtZiYNaj3c4T3ForSghqrP37DI4hFu5+V7FTkOe+M3ipa0hdUNO5l7Y6OFSYNzzQg/6vzGuxHpVaBHAOHr6vl8J5XO20ikFExYyA+kAhLeYQMZ6FILhTJiSq1hVBlhf8XCUFUSqPefFXiBDFkAhhEEKMIUlK2FZOIL0elyo04zDZSrVYy7C7SbBm4bVfhtnmwm11I0smbmyOKqnmnVyXEvE4T8TgmWSLPGXdqC9ZWMXX0CDKOkaipu0QVlU8P1mom9p01bVnpzs5Pj1dIG5XPlEKPVlTlVHD48GF27drFzJkzMZlMvPnmm7jdx88611fowq7ToxxsDvDKrnLumDwUgyQRVdoeC98ZU9Qr612nAkVpYd+WYlSlYyW1rnK8spOdmcFPumZ3NznVYWNd4URixsORCAeNEqaoD6NsxtCuylikLP4/NBd0w2SbC+lXnEXhsEsACMQkSgNm6sNGgopESI3PxNPMChdnx51CD/jMbG2wIyGwGVTsxih2g4rdKBjhugqDHJ/FB5VmrLKKQWpNJGeO/0iX4TCnaFnW5B4Q71bCikJNIBIX8cSaeF0oufyvSZbId1o17/Qch4V0a5uIb/aWdqnk7fHw+kKs2lXBiu1lvLerksZEJUObycCCMQVaUZWCo1QN7G3C4TBz587F5/OxadMmMjMzSUlJ6ZO+dBVd2HV6DF8kxiu74l7PH5bVcXlRJhOyUhie5qTYbeu3o9uuoMbqNVF3e27q9vGSbMHtOXaM8elkBu9JTjZm3CAbk8KwTN2M+RZCUBeKYJRkUhMJjD4s9fJVUzznv0GScJgMZNpNZNktnJUzCoDCmMJ5hSous7FPkxyFY4o2A2+NFa8PJefdN8syhS6b5p2eY7eSZjX1Sr+FEJRUNWrhaBsOeVETnvJFaQ6unzSYeaPyuXRYNrajpGU+FQghkCQJi8XC/fffD4DH0/s+Fj2BLuw6PcK+Rj+fVzVory9MxIhnn2apV92em8gb9vwJHds6I1cjEbaZO64Jnk5m8J6mu97omzdvJqPYxL92/oNJQxYyKq9j/HtMVQnEFIJRhagqKEgkJKr2h9ha20QgqhCIKTQnwsccRgPzh2QzOMVBgcvGpUYDYzJcOEyGTgetVqOhQz7y3ibUKuKt5nR/mIZwsohbDDJFLpsWXpbjsJBmMfXqwDsYjbFub7Xm+Ha4Ib4UIksS5xe3xZaPyUnt8wmAEIIXXniBN998k9deew2j0cg3v/nNPu1Td9GFXadH+LDMS23ChPa1oTnHTOt4JtGZeZ2c3E7b9kcz+EAnJrlpjraJ675GPxurGqjyh4mobYVCHEYD/+/sIQD4oopWcUwiXmp3kNtOkcumhVkOTXUwtI9Mw60EY0pStrbqQJjGI0TcapApdtu08LIcu4XUXhbxVsoa/azYUc7K7eWs2VNJMBp3GEy1mfnWxEHMH53PnJH5ZDj61+BfkiQ2btzIF198wa5duxgzZkxfd6nb6MKuc8L4ozEiikqqxcT5uekcbA5wcUEGjm6Yz4QQRMMHiCek7L/ETjBTXGfm9ZqrvsWEXsxjrhOnJihTZ/4G25sinNe6LRDmcEsQj82Mw2TAbjRgNxpxmtvEv8Bl43tji7GbDFgNcp/PIAEC0VhStrYqf7hDASObUY6XIU1ka8txWEkxG09Z/xVV5fPSuvisfHs5X1S0WfBGZacwP1Hq9IJBmRj72cBfCMGmTZuYMmUKAL/61a+49957ycvL6+OenRi6sOt0mwpfiE8q6tjfFC93Wuiycf3IAkZluLp9Lm/Z/dSV/6qnu9hrHOkAdzzv9c7M6zWbTyxcSqd7NEfjguY0taUbHpfpZqzHfcya9xaDjOUUhk8diT8a02bgrR7qLUeIuN1oYHBKQsQT5nT3KRTxVpqCEd7bXcnK7WW8u7OcWl8YALNBZubwXK4YXcC80fkMOYFnw6nkzjvv5C9/+QvvvPMO06ZNIzU1ldTU1L7u1gmjC7tOt1hzuJZN1W3lDU2yxATPiSeEiIbj5mlXxjeRZedJ96838G/aSPjQQVChdt1qakqHavuO572um9ePTVc83k/UcS6ixkUuy9Lm7e3sQ2eszvBFYknZ2qr8YXzRZBF3GA0MSbGT05o73W7B1Qci3sru2mZWbi/jpX8f5IuXd2jFWnJcNr5z7jDmj85nxvBcnKc4dfPJ8I1vfINDhw5RXHxqo1B6i/71KdfpF7REYqwvryPabg3SZTZxWaEHgySRZjFhNRqYVZxJtt3SIw+YrKJHMVn6rhrSsdi2YCiR8iDm/IIO+3Tv9ZOjKx7vJ5KXvUS1Ut8Uf7yZZXGc1r2PEAJfVGmLEU/89keVpHZOk4FhqY6k3OnHsi6cCiIxhY/31yRysZexx9ui7TunMEMzsZ+dn35KY8tPhoqKCh544AF++ctf4vF4mDJlCq+88kpfd6vH0IVdpwP/rqznS29yvLbHZuayQg+XJn7ONHRv9c452brkvVUlLYyMQQKrshu36dTm5RdC0BKJr4m3L4DSvgIhgMtk5KxUR1Lu9P5iUahpCfLuzrbY8paEU57DbOTr4wqZP6qA/KiX2Rf2n0I43eHtt9/m1VdfZcSIESxZsqSvu9Pj9I9PkU6fs6GyHr+Irx+31jheMCRHq0neUwNxVQ1q5ncAVem8gltfk+TNnlgnP5M5moD7D8ULj5xoDvWerJK2p8FHps1CqtXERClAVpGJf+38GFnqvQI2QgiaI7F2M/F4SdLgESLuNhsZnuqIJ3tJpF7tjpNpbyOE4IvyBlbuiMeWbyz10lqEbXC6k2+fO5T5o/K5eGg2lkQI3+bN/fO7ezRqa2vJyMhAlmVuueUW8vLyWLBgQV93q1foP58snT7lw7I6wMHlwDiPm3ynjcEp9h5PUFG6fSZBX0eBkKT+9VFM8mbX18mPajJ3dDPZS28RVVSWH6jGYpD54fhBGHrBIiyEoCnctibemjs9pCRHdKRYjBS6nFq2tmy7Fbvp1Mazd4VAJMaaPZWs2F7Oyh3llCecYQ2yxMVDspk/Kp95owsYmdV/U6d2lY8//phFixZxzz338L3vfQ+DwcDXvva1vu5Wr9G/nqY6fcLB5vgX2pQIOUuzmkmz9o5XcDRShmxw4c64Tttmsg7BaO48trsvGejm95M1k7ent0zmXSUUU/BFFWxGWZvpflHThDcYN3FXB8JEFJXJWSk9IkJCCBrCUW0G3romHj5CxFMtJga57UnmdNspTkrTHQ7V+1i5o5zl28tYt7eKcCz+ftLtZm6YHM/4NntEHmmnWWKp4cOH43a7sVhOr/d1NHRhP8MJK6qWBtZ1imLJDcZMcoY8fUqudSZzsqlY29OTJvPusrm6kQ8Ox+uJX1bo4dycNAC217dQ2hLU2hW5bJyT3f0QJSEE9aGothYer2QWHyi0J81iYkiKXQsvy7ZbTnlmue6iqCobDnm19K0lVW0RLeNyU7VSp1OLPRhOoC56f0UIweuvv86wYcOYOHEi2dnZfP7555g7yfh4OqIL+xlEbSCsPQhzHFbynFY2VbclkRgunXx5S52+pf0sva9n2SeLEIJttc18WBZfxx/nceNpF18+oygTQTym22aUMXZBmNRWEW9Xwaz6iCx0AOlWEzkpDi1bW7bDgsXQv0W8lYZAmNW7KlixvZxVO8upD8TD/SxGmbmj8uMm9lH5FKf3z/DSnqCkpIRbb72Vc845h9WrVyNJ0hkj6qAL+xmDPxrjf786rL2+IC+dPKeVYpedipQQF+ZnULHzqx67XjRSjtJJiVMhIkhS//2CtTrNDVSHufaz9L6cZXcFRRU0hqMcaPLTHIkRiCnMLM7CYpBpDkd5tuQQMVVgMchcOSyH4WnJQpR1HHOxKiAmpVIasFB+qJaqQIiaQJio2hb+JgHpVnNSLfEsu2VApUQWQrCzplkrdfrJwVqUxHvMT7Fz6/nFzBuVz/RhOTgGUGx5dxFCEIvFMJlMjBs3jocffpjZs2cPeP+AE0EX9jOA6kCYv7YT9YVDc8hIzHwKXDa+4coHoKKHrhcObOfAl+OPut9g7T8ZnY7MHNc+4cxAdZjrL7P0qKLSFInRHIkSiqmMTmQfO9DkZ+WBmg6JWACm5aVjMZixGg14rGY8NjMXF3iOG8utCoE3GNGyte1T7fgOGVDMV1PXANCIBGTYzAmnNivZdgtZdosWBTKQCMcUPtzXVlRlf128VKwkwXlFHuYlYssn5KWdEcLW1NTEj370I1JTU3nyyScB+P73v9/Hveo7dGE/DYkoKqUtQUyyRJHbTqUvRJrFREM4yvfHDdJKT/YWsWglAFbHFKzOczvsd6bO6tXrH43O0r8emTlOTzjTNdREmFdTOIpBkrTKaF/WNrG1pommSCwp5MskS4xKd8bLYBoMyFI8FbHbbKTQZSPLZsFuMmgCbjbI3DymqNNrK6rAG4okFUCpDYSJifYzcQNukyAc3MNZ6XlMyBtJls2CaQCKeCuVzQFW7oh7sL+/qxJ/Is2sy2Li6vFFzB9dwNyReWQl/hdnEjabjYMHD+JyuQiFQlit1r7uUp+iC/tpyPryOj5PpH0dne5iwdAcitw2XGYjplPoIONMm4+n4N5Tdr3j0ZmJXRfyrvNJRR2Hm4M0haM0R2K0ymix28a1I+L3NKSo1AYjuM1Gsu3x/OUpFhNusxFB3PSd57TywwmDu3RNRRXUBtt5pvvD1AYjKO1EXJYg02Yhy24mFNiFUa0j0lKB1QTeWBmDnQvJdw48sVNVwZbyem1Wvqm0Ttt3lsellTq9cHAW5n7uxNcbeL1edu3axbRp0zCbzbz22mt4PB7k08gJ8ETRhf00ojkcZVeDTxP1qblpjEqPmz/Teyl8bSBw5Lr5QA5h6y2CMQVfJEZLNIY/Gv/7YHOAApeNi/IzgLbKaE6TgTynlRSziRSLMWmte3JWKlOyT6ymdkxVqQ0kzOmBVhEP025JHIMkkWkzJ4WXZdrMGGWZen8lb299V2vr84GETIp94GRK9IWjvL+7kpWJ2PKqhLOrUZaYPiyH+aPjseXDM0+8PsPpQDQaZdasWTQ0NLBhwways7PJysrq6271G3RhP42oD0dZW+rVXl+cn3FK1teEEESC2xEi7n0bCe3t9Wt2ldJ7fkbV734LDOx182PRmSd8K0IIaoMRfNEYvkgMX1TBn/h7VIaLkYmB3+t7Kij3dYyKSGnnbDV3UDYLhkjH9D43dDFFYTQh4q1JXqoCYbydiHh2u+plOXYLHpvl6NdIzOKH55yLsTGXCRMnIEtGTIb+PajdX9fCyu3x2PIP91VrYXaZTguLzhnC/NEFzByeS0ofVpzrb5hMJpYsWUJTUxMez8AZuJ0qdGE/jciymblyWDzRS77TesqcZhqr/0j1wR912C5Jfe+B27qmnvPjJQPe3N6VtK6G84bhv20m3mBECw17YfvhJMFsJd1m1oT9rFQnWTYLDrMBp8mI02QkxWIko52l50RjtqOKSk0wnJStzRuM0L5LRknSZuCtzm0ZVnOXBwrtMUhGDJIZi9F+Qv3tbWKKyqcHa+Ne7DvK2VHdlpp1Yl6aZmKfUugZMEVVTgXLly/npZde4oUXXsBoNHLjjTf2dZf6LbqwD0AaQhFqAmH+XdWANxjRwndmFWdydtap9ziPReP+9K70azCa8wCQZCvuzJtOeV86w1xUPOBFHTpPOCMMMqapw0i96jyyF13EuwdrAKjwBfHYzEiSxLnZaZgMMk6TAafZmBBuQ1KGtPNy03qkjxFFpSaQnK2t7ggRN8kSec5ECdJEKdIMm7nH0xf3J+r8YVbtLGfF9nJW76qgMRi3btlMBq5ICPm8UfkUpDr6uKf9l7fffpu1a9eybds2Jk+e3Nfd6dfowj6ACCsqZllif1NAy8QF8Zm6ySBrBVv6irSc/4fdfWGPn7czb/auMlDj0TvDP64Q/40X4v72JcwdnI0sSVT7Q/x1e2m8QULUAYa2E4hLeqkaX7hVxNt5p9eFIkltTLJEvtOqCXi2w0KG9fQWcYgvgXxV1ciK7fFSp58d8qImlgqK0hxcd/Yg5o0u4LJh2dj6UTGY/sbOnTsZOXIkAI888gh33HEHI0aM6ONe9X/0T9QA4XBzgJd3lfOTSUMpdNmYUZQJQKbNTJG7f5oce4qTSRjTnwu4HGlaVy1GItkpWErrkYQglmKj8vszUNw2lBQb0XuvBKC+roWL8jNwW0ykWkyM87hJsRhxm03YjQY8NnOPVw4LJ/Kxt1UwC1Efiia1McvxwWVrLfEcu5U0q+m0F/FWQlGFdXurNBP74QY/ALIkcX6xJ56+dXQ+Y3NOzLnwTOOhhx7i8ccfZ8WKFUydOpX09HTS009tCd6Bii7sA4T1FfUIYHeDj7Ee93Gzbg0EujoTPx292RtCEbZGIgRuvBB1aDbRnBRiiRSfQ29/HpO3BSmm4p88GGIKxuYg1j1VDGvwM+17l+NMxHtbjAbmDc7u0b6FWkW83Zp4QzhZxC0GmSKXTateluOwkGYxnXGCVd4U0PKwr91bSSASj91PtZn51sRBzBudz5wReXicZ3Zc9YkwY8YMPvzwQ9LSemaZ6ExCF/Z+zoEmP/8q9VLTuiZ3GsWrdnUm3p9n3V1BCMHB5gA1gQjn5aZR8+S/2Nmyjtq72+qEp5iNpFpNpFvMTP34AdwWE0IIwoqKxSD3mmAGY0qSKb06EKbxCBG3GmSK3bYk57bUM1DEIR5b/nmpVzOxf1HRVmthVHaKVur0gkGZAzoZTl/Q1NTEww8/zM9+9jPS09OZOnUq77333hn5OTtZdGHv53xe1aiJeqHLxpCU08vsfrrNxNujJgT947I6qgJhAMZ6XLSs2Q2+CHlmI4PHFDL1nis7DSGTJKlHq4cFokpSeFm1P0RTJDmtq9UgM8ht19bDcxxWUszGM/rh2hyK8N6uSlZsL2PVzgpqEmGBZoPMzOG5Cce3AoZ6XH3c04HNyy+/zJ///GdcLhf33HMPwBn9uTsZdGHvZ2yqbmRfo59BbjvjPC7OzUnDYpC5tNCTFFM80Cm952dEDh/SUrkOZGoDYZoiUQa57RhlmaZwlLf2VVLTFEBJCLPr092krv2K93ZUEKtpwVmQwcKXf9JrffJHY20lSBMz8uYjRNxuNDA4JSHiCXO6+wwX8Vb21DZrJvaP9lcTS0Se5LhsfOfcYcwbnc+Ms3Jx9XJ65tOd5uZmXC4XkiTxve99D5vNxnXXXdfX3Rrw6MLez1iT8HY/2BwgrChcXOBh0Gk2S4e2+PKBZmJXhaAlEs/O5g2G2dfoZ3dj3ElqyP/7K+baZlSLiaq/3ILxcB3WbYfwbN6PbV+bx7oxy9WjVdd8kZgm4FUJk/qRBVYcRgNDUuxa8ZMcuwWXLuIakZjC+gM1rEhkfNtd26ztm1yQnogtL2BSfroeW95DbN68mZtvvpk77riDxYsXYzAYWLRoUV9367RAF/Z+QkRR+dOXB4F4+sgfnz2kS/Wl+wJV8RMKbNNeR8NlJ3SegRhf/t6hGnY3+AjG2up3W/dWYV39H+REqJccjjL85meQVEHRNVM5+/2fJ51j8+bNnH0CcbhCCHzRI9fEQ/iiSlI7p8nA0BRH3DM94dzmNBl0ET+CmpYg7+6sYOWOct7bVUFzwsvfYTaycGyhFluee5pHnfQVeXl5RKNRWlpa+rorpx26sPchoZjCxqoGLi7wEFIUAolqWNPy0vutqANU7L0ZX8ObHbbvve5axIGueesPtPhyIQSSJDFnUDa5Dit1wQipVhN5Disbrv09khA9WipVJCwDVe0EvNofxh9LFnGXychZqQ6tlni2w4JTj4vuFCEE2yoaWLkj7vj278Pe1iy0DE53aulbLxmajeU0clLtT6xdu5asrCzGjh1Lbm4umzdvxul09nW3Tjv0J0AfEYop/G7rfiCezjPXaWV6oYcRaU7c/XwtXYnFzcoZeXcCUP3nZ4iVtSAd7Hou64Hk6S6E4PU9FQxLdSIeW67FnrcApUDwiGxwJ3L+5khME/DWtfHAESLuNhsZnuogu9Wc7rD0eLz66UYgEmPNnsp4udPt5ZQ1BYB4TvuLBmdpJvaRWW7dotHL7Ny5k2uuuYazzz6bDz74AEmSdFHvJfSnQh+xtaYtP7TdFJ8dTMkZSPGaBjKLfglAxf+9jETaaevd7o8p7GsK4I8qpLy2oYOQ2wvSu7xmLoQgICR21re0eacHQkmmfYiHvw1PcybypsfN6a2fE51j0xKOsWZPPfdvXcu6PVWEEgOkdLuZ6ycNZv7ofGaPyCPtNMgFMRBQVRVZlhk5ciT33nsvM2bM0AdRvYwu7H1Ea+7sKwZnn1be7qcj3kB87XxwioMG4kLeFbO7EILGcDSR6KXNnB4SLthXpbVLtZgodtmTSpGeTvkKehtFVdlwyMsHu3ZS7IS/btzLS/+JF0Mam5PK/NH5zB9dwHlFHox6bPkpIxAIcN999wHw2GOPAbBkyZK+7NIZgy7sfUA4pvBxeR1An8/ChFAI+T5HiAiyuptAs/+4x6ix5uO2OV0QQlDmi9fEzrSZaThGu4ZWEU84t1UHwoSV5Jl4msVEihJmVEFuYiZu6dFY9TOFxmCE1TsrWLGjjFU7KqgLhClwh3jgcihOd/Czc3L4wezzKU7XTb19hcFg4LPPPkOSJAKBAHa77oR4qtCF/RTSHIlyqDnIILed83PT+LK2mcw+Ngc2VD1FzaE7ALABh7d37ThJPjO+pL//Yj/BmIoE5Dmt7AaEJOENRpLWw6v9YSJqsoinW00MTXFo2dqy7RYsRgObN29mcg9VUztTEEKws6aZlYk87OsP1KAkYsvz3DZumXoWs4dbaGrZx9fGFGJsTNdFvQ/w+Xzs2rWLyZMnY7FYePnll8nKysJi0Zc9TiW6sJ8iFFXwzLaDpFlM5NgtXFzg4eKC3qm61a1+ReNx827PIrz1RnJzc7t0nNVxjpbrfaB5uHeHQpeNYEzB79/Fi1tKiN2/kOjgXHaVHGprJFQMogmrqMMovJhUL0ZRjxyOUtcEdcCR46X/rH/1VL6N04YMEywaH/9JZhNNetRUn6IoCnPmzKGiooLPPvuM7OxsCgsL+7pbZyS9Kuy/+tWv2LZtG5IksXTpUsaPb/s2/v3vf+ftt99GlmXGjh2rpRA8HSlrCfL3nfFY74ZwVCvg0Z9Izf4ulU1WMgu7Hl+9/42faKI+UDzcj4UqBN5AmDf3VWE1ysiSRE0gnKh3Xxz/toxQMZV5cQ5WsEkt2OQWbFILstR+tm5P/HSOz9eC06mnH+2MiKJSHwhTHwjTEIigJOLRDJJEmt1Mht1Cmt2C+Shr5bJkYHDmBEobvaey2zrETe/f+c53KC0tJSUlpa+7c0bTawqzceNGDh06xCuvvMLevXu5++67efXV+CzF5/Px3HPP8d5772E0GvnOd77DF198wcSJE3urO32GogpN1AHmDc4e8I5RR87UB6I3vKIK6kKRpGxttYEwsdbA5jBIgMdmJsdu4WDVarIdFuRv/Rs5EjupmPXNmzczeXz3E9ScjqiqYGt5vVbqdFNpHRA3oQ/zuLhidAHzRuVz0ZAszN343pSiC/up4NNPP+XFF1/kD3/4gybsOn1Prwn7Z599xowZMwAYNmwYzc3N+Hw+nE4nJpMJk8mkOVQEg8HTeoSX77RS7gtxx+Sh/TrxO5WJwgAAIABJREFUTFdpL+onOlOvC0Y6pD3tTQTQlHBuqw6EqGk3GwSQJciwmglWNuBLdZC28gsy//EJclQhCqSEG4hKJkRN5KRi1nXAF47ywe5KVmwv592d5VQ2x50TjbLE9GE5zEt4sQ/PdPdxT3WOx3PPPcdbb73FokWLuOCCC/q6OzoJek3YvV4vY8aM0V5nZGRQW1uL0+nEYrFw++23M2PGDKxWK/Pnz2fw4MG91ZU+QxUCWYLZg7JItZgGnKgfrV76yc7UA9EYz5UcQhy/aa9gkCQybWYtvMwgw8fl9TSEosRSHUiBCKnv/wf5iFSt0L2YdZ029te1sHJ7Ocu3l/HhvmoiiWgBj8PCTYmMb7OG55Ji63qSI52+oaysjIKCuE/Nr3/9a37wgx8wZcqUPu6VTnt6TdiFEB1etyYl8Pl8/OlPf2LVqlU4nU5uvvlmdu7cyciRI495zpKSkt7qbo8iBGwRdhoSt3e61Ex/rRthUqowA7t27gJ5Aps3b9b2qa+8BLU1kJmVfFBmFpELLkxq2x0CQkLgwoVCphQ9/gE9hAWBCwUnKnIICIFSBzEBVmEDZKS3tpLyziaG/u/12nFCCEpCr2GWMymwXIoKJ/zeWznZ4/s7MVXwpTfA+nIfn1T4ONAU1vYNT7MyLc/JhflORqfbMMgSxLzs3d6z5vPT/R73Ba+++irPPfccv/3tbxk9ejQHDx5ElmX9Xvczek3Ys7Oz8Xrbvqg1NTV4PHEv8H379lFYWEh6etykec4551BSUnJcYR87duyACJvY0+CjYW8lAGdnpTCl+Kw+7tHRqT2cQ10FjBg5gh27YXK74iTbzGbohTX0hlCET/9ziMGeNOYOzu7Rc3dGVFWpC0Y43BKkMRylPKoQjMVz8y8cmkvp/a+SkUgTG0hklWt/H4RQKfnkNVwuF5PHnfza+ObNm5POf7pQ5w+z6v9n777joyrTho//zrTMZNJIJ50EkIReRRHpZWkiWBDFimsBYVkFFQsiIoqLvHZFBRRZF6UIKiqIFUGaPCCEXhISQgrpbep5/5hkSCDJpEwyk3B/n4/PZ+fMmTPXBMg15y7XddTWHe37o+fJLbEV9tGplfYiMaM6hBPZSt/osbTUn7GrGQwGfv75ZxISEgDEz7iRGQyGet3QNlpi79evH2+99RaTJk0iMTGR4OBge13g8PBwTp06RWlpKR4eHhw6dIgBAwY0VihNblNZVbEQTw+GRwc7OFtobGfyitlQ9kWrIq1SgcFiIXntn/aELobaa0+WZQ5fyC1rqpLKjrOZWMtG6iL9PJnUPYbRCREMahuCTtS0b5ZKSkp44403ePjhh/Hz8+P666/njz/+QKVSibt0N9Zo/9p69OhBx44dmTRpEpIkMW/ePNavX4+3tzfDhg3jgQce4O6770apVNK9e3d69erVWKE0ufKV1ePiQl0cSWVmUyalRX9VOmY0nK70uOK8enPan26xyhitVowWK0arld0XcugW5Eu4l462fnp6h/gRoNMQpteiUyvRKZW2IWBgL7UvE3u1KzVZ+PnkBXuHtKQcW6VChSTRNzrQfmfeKdRP1ANvAVavXs3ixYspKipiwYIFAKhU4kuau2vUP6Ennnii0uOKQ+2TJk1i0qRJjfn2LjPpmnBKzRb8te61ECj1+ERKCnZU+ZxC0gKVV7y70/50WZY5llPI31n5lJqtdA7yoVuQbSfFuhPnOZl7ZSlck0UmvK0OhSQxOCqoqUNuMVLziu2JfNuJNIqNtkWFvlo1t3WLZnRCBCOvCSPQS+viSAVnMBgMaDQaJEni3nvvpbi4mAceeMDVYQl1IL56NYJoH/cst2ox5yApPAkMn1vpeN7Xv3LsgVuxGkwYMzPcam/6oax8jmYXkFViJM9o2x4nATG+l37GQToNJqsVjUKBRqlAo1DgpVHRM7jlbqFsTFarzN6Ui7a95Ymp7E/Ntj/XIdjH3ur0+pgg1KKpSouSmJjI/fffz6OPPsrdd9+NSqVixowZrg5LqCOR2J2ovMd6lLeOOzq45xC2QqEnIPwpoEKhmeSy8qihrV1+l55eVEqByUxbP9t6jBO5hZzKK8ZTpaS9n54BkYG08lBXGuZtSGne/XNWU5SUhT7a9eV9XSm/1MjW8r3lR1LJKCwFQK1UMLR9a8YkhDMqPoK4QFExryXz9fUlPT2ds2fPujoUoQFEYneiC0W2LT0ZxQYHZ7oH+7B7VDT+N08kY8LtdHXBKleTxcp3Z9M5llNIWV8P5vRqiyRJ3BgeyODIoEZrbZtcthr+alwwdzIrn28TbUPsv53OwFS2tzzEW8t9feIYFR/BsPat8daKtsIt2f79+9HpdHTo0IHw8HD27t1LQECAq8MSGkAkdiewWGX+ysjlp3O27X29Q92vc1fSgkcwDjuCXKDgQEIccGWhmQwXrXLdk57LkexCvNQqQjw9iPTWImMbcg9ogoIl+uhAui++s9Hfx9VMFivbz2TYh9iPZ15qv9szwt+2HS0+nJ4RASjctfCC4FSnT59m+PDhdO7cmR9//BGFQiGSegsgErsT7M/Msyd1gBg3nGMvil+JAsBw6Re2q4fdwbYo7mBWHmqFxNROUXg08zr67iazsJTvjtq2o205dp78UltBIL1GxU2dIhkVH86o+HDCfN3v76zQ+GJjY5k5cyb9+/dH0cwqYwrVE4ndCXqF+BHlrSO1sIROgT6o3fAfiORlG2aNHvATnon9XBzNJXlGMyaLzDWtvJya1PfPWW0fZq9J+f71lkKWZQ6m5diH2HclZ1FeBDLGX8/dvWIZFR/BgLgQtGrxJepqYzQaWbJkCQUFBbz88ssAPPvssy6OSnA2kdgb6GRuITqVknAvHcGe7l0Vz3pBhWdf90nqAH4eah7t2gaj1er45DqoWHSmJi2hIE2x0cxPJy/wbWIKmxNTSckrBkCpkOjfJphR8RGMTggnPsRX7C2/ylmtVjZt2kRxcTFPP/003t5iMWRLJBJ7A607YatoNiY2hI4B7teNqnzlu/o1C+7yx13eRyCtyLbyWqmQ0Cmcf/fYkovOJOcU8e0R21z5zycuUGq27S1vpdMwuUcbRsWHM6JDGP5u/mVTaHwmk4kTJ06QkJCAVqtl1apVBAcHi6TegrnHb/pmymC51P0rwd89/5Fkb1iHST6HRwBIhV6uDgeAwrKuaRdLjWUd8NzrLrLYmE9WwTn748sbGrmCxWplV1IW3x5JZXNiKgfTcuzPdQz1ZXS8bW953+hAVGJvuVDGarUyduxYTp06xY4dOwgKCqJt27auDktoZCKxN8Cx7EIA1ArJrYY4Ly8L67XGFpsmOMqVYdmV90H306jdLqkD/Hr0v6Tnn73iuErRtJUEc0uM/HD0PJuP2PaWXyzbRumhUjCiQxhj4iMYlRBOjL97fGET3I9CoWD8+PEkJiY2iwZagnOIxN4AyrKk1Le1ey2+urwsrOSdAkDruI9dHJmN/QbY/XI6AEZzKUqFmh7Rwysdj/SPb9T3lWWZYxn59vKtv5/JwFK2sT/MR8fUvm0ZHR/BkHah6BtpX7/Q/CUmJrJixQpeffVVFAoFDz30kFvdeAiNTyR2J9C74eriivvTj+32xkPXEa2+u4ujspGxJSt3/lWjVKjoGN6/0d/HYLbw26l0e4e0UxcLAJAk6B0ZYCvfGh9Bt/BW4pezUCtLlixhw4YNjBw5kiFDhoi/N1chkdgbIFSvZVh0EOGi+UWTqO0WNnDvbWwXS8ys2H2SbxNT2Xr8PIUGWw18Lw8VE7pEMTo+gn/EhxHirXNxpEJzkZ2djb+/7e/7okWLuP322xkyZIiLoxJcRST2evo7K59obx3dg9xnC5EsyxTn/4yip63TWf7FdWXHza4M6wrl+/x1ddy3XtstbOBe29isVpn9qdn2IfY95y7an4sL8OaBa8MZHR9B/9hgNKJAj1BHn3zyCc888wybNm2iR48eBAcHM3z4cMcvFFoskdjrafOZdMBW09xVKi6SA5BiDWievYB6mu3x+RO3259TKPVNHV61vDW2v3b1aWvbXLawFRpMbDtRtrf8SCpp+SUAqBQSPUM8uePaeEbFh9M+yMdtvhgKzVNsbCw+Pj4UFBS4OhTBTYjEXg+ZJZeavLjyl3LFRXIA6GxFXiz/p0On7EursePt53r6DnVFiFeVMxcL2HwklW8SU/nl5AWMZU1VAvUeTOkVy+iECIa3b83JxL/p2TPBxdEKzZXFYuGjjz5i0qRJ+Pr60r9/f/bt24dOJ6ZuBBuR2Ovgz7RsDmUVcLHUCEBgEzQocaTiIrmi3C2cOzqKkLFzCQx/2sWRVa+wrK96vtFU43mXz6m727y52WJlZ1KmvXxrYnqe/bmuYa0YXdbqtE9UAEo3LDMsNE+rVq3i6aef5syZM7zyyisAIqkLlYjEXgdtfPVcKDJQbLZgscqMiw11SRz2PuoV79abCVmWuVC2H7vIZKnx3Mvn1N1h3jy72MD3R8/zbWIKPxw9T06J7UueVqVkdEK4rUNah3AiW7nP1IfQ/FksFhQKBZIkceedd5KSksKjjz7q6rAENyUSey2lFpYQqPNgfNvWTfq+huLDGIoPVTqWm/op1rYX0fYJwKt3R/Kz1pSde7BJY6uPdSfOc6qslrmqFq1BXT2nLssyiel59lanO85mYi3biB/p58nt3WMYFR/OoLaheGrEPyfB+c6ePctDDz3EPffcw+TJk1Gr1aJxi1Aj8ZuoFopNFj4/moqfh5o7OoSjVzfdjy35yDAspoxKx9QPg608SRYGNnP+5OZKzysU7ne3mFtq4ovjqeQYLg2/B9Zj8VxTsMoyJouVx9bvZvORFM5m23YZSBJcFx3EqHjbnXnn1n5i4ZvQ6JRKJUeOHGH37t1MnjzZ1eEIzYBI7LWQZzRhkWVKzJYmTeoAVnM+Kk00AeFz7MdS5j8HQMS8BVecL0lafAJc22PdZLWy83w2OQYTQ6KC8FKr8NIoMVisxPh40qGVnu+TMt0qKZ7PKy6rw55Cr9A8/LRm3v3jGL5aNbd2jWZ0QgQjO4QRJGoWCE3g9OnTWK1W2rZtS2RkJH/88QeRkZGuDktoJkRir4XymvARLioYolIH0yrkIfvj5F8WA9Dq3Yeqe0mTO5ZdyMGsPExWmQtFpZjKSqFGeOnoGeKHSqFgerc2SJJETtniQ1eyWmX2ply0b0f7KyXb/tz14Qo8VAq2PTKMfm2CUYumKkITOnfuHP3796dDhw5s2bIFpVIpkrpQJyKx10JSvm1OuEug+7VldRcXiks5XTZ3DqBRKpjYtnWlL0OuvkPPLzWy9XgamxNT2XwklYxCW9tYtVLBkHahjEmwNVU5nLycImMeA9u6ZnGkcHWLjIzk7rvvpk+fPiiVomCRUHcisTtgrbCKu7XeucOwVmsJhTmbka2l1Z4j415V4y4nyzKSJNErxI+uQb74NbA5ScUtbs7Y3nYyK9++He230xmYyvaWh3hrubd3HKMTIhjWvjXe2ktxH05u0FsKQp1YrVY+/vhj0tPT7YviFi1a5OKohOZMJPZauKVdGMdyCtGpnDskm5exgvSzMxyep1C6X1tOqyzzf5l5HM0u5Pb2DV9QWJ7Qi5KyANBHB9Zre5vJYmX7mQw2lyXzY5n59ud6RPiX9S0Pp2dEAIparMoXhMZmNBr56KOPyM7OZvr06fj5+bk6JKGZE4ndAYUkEeenJ87P+SvNrRZbCchWoTPw8OxY7XmePoOu6LHu6v3r21MvsjMtB41CQVaJgZAGjmaU71nXRwcSdUtfui++s9avzSos5buyveVbjp0nr9S28t5To2Rcxwjb3vL4cMJ8PRsUoyA4i9VqJTk5mZiYGLRaLStWrCAgIEAkdcEpRGKvgSzLLN57kgR/b8bGNd58q95vOF5+I684fimZv4wxOQkATVQ0mvAI/G927cr3pPwSFBI82CUaLyftFKjtnnVZlvk7Lde+t/zP5Ex7j/cYfz139bSVbx0QF4LWDVvqClc3WZaZPHky+/fvZ8eOHQQEBJCQIEoMC84jEnsNzpQtmkvMLmjUxF6ditXlNFHR+N88kciFi5s8jqpklxrx81A3KKlf3H2SjUNewirLlKRmowzR8fvxL6o812KVScsvISWvmJTcIoqNtqp1CYEwMEZLhK8n4X6e+OnMwFngLHvO1C+uIkOubdO6IDQCSZK44YYbUCgUWCw1V18UhPoQib0Gm0/bOrh1D/J1WQwVa8G7iz0Xcii1WAn3atj2v8KzmShTslG31iMHKTDeqOJUxl81viZIZ/uvKhcLbP85Q4A+3DkXEgRsW9hWrFjBs88+i0Kh4NFHH2XatGku3ykitEwisVdDlmWKzLZv031CWzX6+13eghVcO5dutlrZcyGXQxfzKTXbVpLf3ykKvVqFt0aFRqmge3Ddv/Dsn7Oak78fgTfvwVxkwDPCn/bbJ7Pr9Eb6tBlLVkkIP524wE8nL3A041JTlfZB3gxq25pBbUPoER6AUtm4vxA9NWJro+A8L774IuvWraNXr16MGjUKhWgKJDQikdirsT/zUlLx0zZsC1dtVNXUxVVz6TmlRlYfSaHIbEGjkPDSqJDK/g+gg783sb56NPUo3JK89k9KyubDVXoPgsf35q+UiwDM+OoQP58+Ddj2wQ9qF8Po+HBGxYfTJsDbOR9OEJpIUVERer1t0e2CBQsYPHgw//jHP1wclXA1EIm9Ggn+3vhoVHg0YYEIdxl2TysyUGS24K1W8UDnqCp/BvVJ6uXUwbYkfbhzFDOzEum/+yR3dQOlQmJq37aMio9gSLtQvBq4J14QXGXDhg3Mnj2bdevW0bVrV0JDQ7njjjtcHZZwlRCJvQoWq8zJ3CI6XaWV5nw0KhL8vekS5OOULzZ7n1jFyTU7KTFZUF8sILOs+Utafoltb3mCFrjA+7f0pU1Qlwa/nyC4mp+fH2azmZSUFLp27erqcISrjEjsVTiQlcf21It4qVXEXLb3+ffT6RzNyK/mlVfScxRvDuL76xY8T1Ruv6ruasCjNyTedzek5GEKCuHDP0845TPUR3JSDn+ZLr3/hcyGrUQzmCz8ejqdGz/+Gb+CUvK9tCi8tOT3iEELTO4Rw4T24Rw5v5Ndp/eKhehCsyXLMmvXrmXEiBH4+PgwaNAgDhw4gK+v6xbeClcvkdgvczavmK1JmQCkFZVWSuwGs4Vh7/9oL0taG9/9Yw5RXpkwANt/VcjNNlPq6ce2kC68/eWfDQm/4XanOf2SgxQS5kBvOv72Av1jgyk2W1j2dxK6Ju6UJwiNZc2aNTz66KNMnTqVxYttW1JFUhdcRfxmLSPLMrsv5PJLSpb9WM+QylWgTBYrJouVrmGteHxg7QpKhOcXYM1RUPqBjNXHj8zHnqn0vBk/8pfcAJKCPsCnDf4k9Xf2zFli2sRQIssYZBkvSULVgNto6c3v0fxyGFNBKZ4R/gxt3xqAYrPYuys0f3JZVSRJkpgwYQL79u3jsccec3FUgiASu12ByUxSwaXuZE/0bIuymlri4b6e3NkztlbXPfKDEWuBFeXxaIJunkiv3v9ySryNYR85dOoWw//7y7aAb0J8RIMa32z8/QjFabn1qvkuCO4sPT2dWbNmMX78eG677TY0Gg2vvfaaq8MSBEAkdjsfjZrxca3JKjES4ulRbVKvD0mlcovV7o4YZInfKoxY1LVTW8XObHCpO1ttysQKQnNSWlrK77//jlqt5rbbbnN1OIJQiUjsZUrMFjKKDUT7NKxRSEHO15gNqZcOeMhQ1MDgmsDmM+n8LXtDRh46lYJ/do5Bq6rbivjyRi7lrVbFnbrQkqSnp1NaWkp0dDTR0dFs2bKFa665xtVhCcIVRGIv8+Xx80jAxHYaPOu5qMtkSCL12M2VjklakEvcv8pUiKcH5zGREB5Kgr93nZN6OXGHLrREaWlp3HDDDcTFxfHdd9+hVCqJj493dViCUCWR2MuoFBLnCkpQNmCxmNVqm6P39B2CX9ADACQ9PgPraQ+ofRfSJpNvMFFithCi19oWCqacomeYf6VzLh9er0nFu3VBaElat27NmDFj6NSpk6jvLrg9kdjLnCsoAcCjnneqFWk82uITaJt3s+55usHXc5bzhaX8mZaNVZYptVhJKyrFKkOPYF+GRQfbz6uYzIuSbHPu+uhAh9cXQ+9CS7JhwwZOnTrFE088AcAbb7zh4ogEoXZqldiPHz9OcnIyQ4cOJT8/Hx+fllWRzeDk7VeFu3dyYEwc4NpGLpf76VwmqYWl9sehnh70DPEj4bI67BXnyvXRgUTd0pfui91wyEEQGonBYGDhwoVkZGRw7733Ehjo+IutILgLh4l95cqVfPPNNxiNRoYOHcq7776Lj48Pjz76aFPE1yTOFdru1vXV3K0bSo5QlPsjBrOFO9v+RYK/L9lph684z2K6YDv/zGmMqSW2PuouauRSlRgfT0I8PRgQEYhCAlUNHabEXLlwNUpPTyckJAQPDw8++ugjvL29RVIXmh2HiX3btm188cUX3HPPPQDMmTOHSZMmtajE3lqvxddDxfWtq54fvnB6GiUFvwEwt7vtWEZS9deTixVu09ClohvCA1wdgiC4JVmWmTZtGj///DM7duygVatWdOvWzdVhCUK9OEzsCoWiUu/gyx+3BHq1ijFtQqstxiJbi0FSExDzKXet3k6PiADmDu1czdVUnH5kRuMFW0+lZgu5BhOhDSg4IwgtlSRJXHPNNZw+fZrCwkJatWrl6pAEod4cJvaoqCjefvtt8vPz2bJlC9999x2xsbWrutYcGCxWLhSVEuWtq3G1qySp0PmN58fUUjQ+4Xj7D67hou5XXW79yTTyDSa6BPlyfVjDV64fS9vFvqTv7WU168KMF6hu4mTGX6y+sAurbG5wPIJQV9nZ2Xz66afMnDkTSZKYPn0606dPR9mErZoFoTE4TOzPP/88n376KSEhIWzatImePXty550tZyHVH6kX2ZOeS6BOwwOdol0dTqNIzi+2r/qP8tY55Zppeacwmkvw8wxGkur2i9Aoe4IF1EoPvDS2OyONUkuQd8v8+Qvu6ZlnnmHNmjXExMQwfvx4kdCFFsNhYl+9ejUPPPAADzzwgP3Ym2++yYwZ7jfcXB8Hs2wtWPs54S7W3eQbTJzMK2Jbsq1bXe8QPyKclNjLDe/0IJ4ab8cnVpBTamTZ30lEB3TkH21qGPkQBCczmUyo1bZSyc8//zydOnVi7NixLo5KEJyr2sT+559/8ueff7Jp0yby8vLsx0tLS9m0aVOLSOzJ+cUYylqwXtPKy8XROF9qYam9Be2w6CC6B9XcRjLjzV/Y+PtKUWhGaJG2bdvGrFmzWL16NZ07d6Z169ZMmzbN1WEJgtNVm9hjY2PJzLQlhYpDVF5eXrz++uuNH1kT2JGWDYC/Vt3galLnnplD9oZ1gPvsXQ/VezA8Ogh/raZWNfALth3HklEoCs0ILZLVaiUzM5PDhw/TuXN1i18FofmrNrEHBwczduxYunfvTkRE5ST16aefcu211zq8+Msvv8yBAweQJIm5c+fSpUsX+3NpaWn8+9//xmQykZCQwIsvvtiAj1E/oZ5aMoqN3No+vMHXyt6wzp7QXb13Pa2oFIPFSqSXju7Bfo5fUIHYvy60JNu2baNPnz54e3szbNgw9u/fT2hoqKvDEoRG5XCOvaCggJkzZ5KTkwOA0WjkwoUL3H333TW+bvfu3SQlJbFmzRpOnjzJ008/zZdffml//pVXXuH+++9n2LBhzJ8/n/PnzxMWFtbAj1M3/cMDuC6sFR5OWjTjLnvX92fk8XdWPvd1jCLY06PmcyuUjzVnFOARIfa6Cy3Dpk2buPfee5k6dSqLFy8GEElduCo4TOzz589nypQpLFu2jFmzZvH999/z73//2+GFd+7cydChQwFo27Yt+fn5FBYW4uXlhdVqZd++ffYh/Xnz5jXwY9Td/oxcvNSqaheTFeX9SGnhXwCYTWn241GBXoQHefNn2TB+3g/fUXzobyw3DEfp40tJ2XFXSiuylY09ll3A6byae8YmGk2YBsSj9vXEbLGg7dbG/tlqct4QSpES9mUUolaa6hRfiZNL+ApCVUaMGMGtt97Kvffe6+pQBKFJSbKDjcj33nsvK1euZMqUKaxatQqr1cojjzzCBx98UOOFn3vuOQYMGGBP7pMnT2bhwoW0adOGrKws7rrrLkaMGMG+ffvo3r07//73v6ud5zYYDBw6dKieH7FqP1pt9e6vlwrwlK78EXiaBiFRbH9sJYiLbOKbEi3+XqLIizNEY6CdwuDqMIQWoqioiPfff58ePXowaNAgV4cjCE7TqVMnPDxqHn2tyOEdu8Fg4Pjx42g0Gnbv3k1kZCSpqakOL3z59wVZlu2JW5Zl0tPTmThxIjNmzOCf//wnv/76KwMHDqzxmnX9cNU5X1gKR84B0K9ndxRVfKE4usuExqMDwdGvAaDRxRMlhfP9ruMYTGZG7NlKwc4/MGdloQoMpM37yxscl7NsT73IhWID4+NCq60Hf2r5z2RuP4ohqwCPQG/6Ln+EkydP0rZt21q9x98pv5Cef4b+7Sfhoar7FjqFBBFeOtTKllXFsDb27dtHz549XR1Gi3PmzBl+++03SkpKGDhwIL169XJ1SC2a+Hvc+Op7U+swsT/xxBMkJyczY8YM5syZw8WLF5k6darDC4eEhJCVlWV/nJGRYW+m0KpVK1q3bk1UVBQA1113HSdOnHCY2J2lfKjaR6OqMqmXU6r88Gr1D/tjo8E25GyxyGg/eANF2WI5/2t7Eumnb9yga+l4TiEXim13wW39vFAqqv58h97+AUVKNgER/kT1bU+cn55cyUxcLT/HuQt55FpTiPHxwFPjHp9duPrk5+dTUFBAeHg4bdq0YePGjXTp0oWDBw+6OjRBcBmHib3iN7Iffvih1hfu168fb731FpMmTSIxMZHTYMzbAAAgAElEQVTg4GC8vGx7xVUqFZGRkZw9e5aYmBgOHz7M6NGj6xF+/WSW2BLfgAYuFHP1YrkTOYWczS8mo9jAhHZh6FTKsq1tOuJ89Vck9YoL5cr3qosV8EJzlZWVxaBBg4iIiOCbb75BqVSKO0hBoIbEXlhYyPvvv8/Jkyfp3r07Dz74IAqFgvT0dJ5//nmHc+w9evSgY8eOTJo0CUmSmDdvHuvXr7dvO5k7dy7z5s3DYDDQrl07Bg9uugpk5elOKdmGgWXZSl7GcszmjEsnye67wCuj2MC+9Fx71TwJyC41Eu6lI1CnYdI1Ve+hr9hnXexVF5q7wMBA+vbtS2xsLFarVZSEFYQy1Sb2efPm0bp1a2699Va++eYb3n77bcLCwnj77bd58MEHa3XxJ554otLjDh062P93dHQ0K1eurF/UDTQgIpC+rf3xUts+vqFoPxfOPHzFeQqV+239OpVbxFen0jBbZbzUSvqEtqJbkG+t56rFXbrQnP3222/8/fff9opxy5Yta3BxKUFoaapN7OfPn2fJkiUADBgwgGuvvZZrr72W//3vf816L2haUSl+Hmp8NCr7LwSrbJtz9w64Hb+ge+3nar16uyJEO1mWOZCVz7HsQia0a41aoeB8USnIMDImmM6BPjWuEaho/5zVFCVloY8ObOSoBaFxmEwmZs2aRWpqKhMnTiQ0NFQkdUGoQrWJXaVSVfrfCQkJvPvuu00SVGP6NNG2Gv6BTtEE6jSVntN4tEHvN8zhNfSFeRiTk9BENV43MlmW+erUBY7nFJY9th2/IcyfjgHe+Gs1Nbz6SuVz62L4XWhu8vPz8fHxQa1W8/7776NSqZr1zYUgNLZqE/vl34RbyjdjjULCaJWvSOp1uobBtr+9McvGFhjNHM8pJFinYWL7MDRlQ+2SJDlM6hUXyZUrTslGHx1I98Utp+Wu0PI9++yzbNiwgT/++AM/Pz9693btKJogNAfVJvaUlBTeeOONah/PnDmzcSNrBNmlRoxWGR+N7WNbLcXkZizDUHy4Vq9Pn/c0rQZNRGG1oImKJnLh4kaL1WS13aK39tLio1E7PL9iMi9Ksm0zrDjsLhbLCc1Rq1at8Pb2JjMzEz+/uvU9EISrVbWJfcKECTU+bm5kWeaTsmF4T5Vt9Wxh7ndkJF1a4KdU1zz/XLBxPcqBE7AqlI3e5CW3bM98dQVmLldxxbs+OpCoW/rW++5clmWyi9IwmkuqPafUVFivawtCTYqKilizZg333XcfkiQxc+ZMpk2bhlYrqj0KQm1Vm9inT5/elHE0iRhvHcdzixgcFQSALNv2s7dq/S+8/W9G5+W4Y51FkigIaO30u3WDxUpKQQmBOg2+Hmp8PdQoJPDVOCw1YOesFe8Z+Wf57u+atzOWU0pii5HgPHPnzmXVqlX4+voyceJEVCpVpfU+giA4dtX8i5EkiV6hregXHnBFxzMPbQc8vfs1eUwWq8zZ/GIKTWZ+TcmixGyld4gf/cL8CdRpuLNDJK311ZfQrargjDOUmmyNY8L82hPkHVnteb66IDzUjvu8C0JNrFYrirKRqdmzZxMYGMioUaNcHJUgNF9XTWL/9VwWHQO9CdTVr9b8uWfmYDqX7NSYErML2HwmHbAVmekT6oefhxpL2RL4MAfNZhq74ExEq/YkhN/g1GsKQkV79uzhX//6Fx9++CEJCQlERETw3HPPuTosQWjWapXYrVYrFy9eJCgoqLHjaRQGi5U/L+RQaDYzuk0oZlMmuenLKC3aW+trZG9YB4BRWf/V9JfLL5tHj/PVc0O4P6H6us8jioIzQnOWnZ3N0aNH+eOPP0hISHB1OILQIjhM7Dt37uSZZ55Bo9Hw/fffs2jRIvr27dus2iKWlvX/Ti2wFaLJy/yErJRLPeCV6tp9YVFHRlGk0dZrmEOWZc7kF5NTaiK92MDQqCBifD2xyDI9gv3wquVcemMNvwtCU9m7dy/x8fHo9XpGjBjB7t27iYuLc3VYgtBiOFxy/e677/LFF1/Y79Yfeugh3nvvvUYPzJmMFisA4WVD27JsBCA4+j/EdN6LV6txjfr+F0uMrDyczJfHz/NjciZ/Z+VjkWXCvXTcGBFY66QOl4bfQWxhE5qfrVu3MnLkSBYsWGA/JpK6IDiXw4yiVqvt7VYB/P39Uasd76t2J9mltiHvQlPlxi4euo5o9d0a9b33Xshh2znbvvL2rfTE+erx12rwqEMfctGVTWgp+vfvz7Bhw7jppptcHYogtFgOE7tGo2H37t0A5OXl8e233+LhUb8FaK5S3r001rfuK7jPPTOH7A3rMKamoA4Lr/PrS8tGC2J8PBkf19phBb+qqsZVLDgj7tKF5qS0tJRXX32Vbt26cdNNN6HVavn8889dHZYgtGgOE/u8efN44YUX+Pvvvxk+fDg9evTgxRdfbIrYnMbPQ020t67WDVMqKk/qmvAIvMbdXKvXZJUY+DurgAERAdwQbvuvtiqudC/X0IIzguAq58+f54MPPqBjx46MGzeuxZSmFgR35jCxnzx5kvfee8++z7S5yTeaCNRp6BLkQ5R3/fZca8Ij6Jp4ikKDCXYdr/Hcs/nFrD1+HossE+mtpa2fV53fTwy1C82ZwWAgLy+P4OBgYmNj+d///kePHj1EUheEJuIwWy9fvpxBgwbxyiuvcOTIkaaIyak+O5LCf4+mkBDgU6dFanWVVljKt2cusPb4eQDGxoYQ66tvtPcTBHeUm5vL4MGDuf/++7FabdNQN954I15edf+CKwhC/TjMdCtWrODixYv88MMPvPzyy+Tl5TFmzBj++c9/NkV8DVZoNFNitjg+sYLyeXXAPgxfkwtFpaw6cg4Z27D/0Kgg4vxEUheuPr6+vsTFxREUFITRaBQ13gXBBWp1CxsQEMDkyZPp1KkTa9eu5YMPPmgWiV2WZWQgxLN2i/3sC+WSkwDQREWjCY9w2PCl0GTG10PNtaGt6BrkU+8hx/1zVlOUlFWpK5sguLuDBw+yb98+e+OW5cuXi/ruguBCDv/1HThwgO+++45t27YRFRXF2LFjmTNnTlPE1mAry7q5WcpaoDpiXygXFY3/zRNr3eilrZ8XcWXD7g2ZRyxfDS9WvQvNhcVi4YEHHiApKYlhw4YREREhkroguJjDf4ELFixg3LhxfP7555X2s7u7YpOFjGJb97aeIbXv41y+UK6u6pPQL9/aVpySjT46UKx+F9xeaWkpWq0WpVLJG2+8QWlpKRERNU9ZCYLQNKpN7ImJiSQkJPD4448DcOLECU6cOGF//rrrrmv86BpApZAYGBGIh1JBp0CfGs+tuFfd0Xz65Q5k5pFrMNE7xA9Pdd3uVC7f2ib2qAvNwdKlS1m1ahW//PILPj4+XH/99a4OSRCECqrNRF999RUJCQm8++67VzwnSZLbJ3aNUsG1rVvV6tyKSd3RfPrl/srII6vEQN/Q2r3X5cTWNqG5MRqNGI1GkpKS6Ny5s6vDEQThMtUm9rlz5wIwbdo0+vatfBf5448/Nm5UTrD3Qg4mq8x1YTU3STn3zByMyUlooqLrPASfUlBCRrGBSG8dHiplQ8IVBLdlMpnYtGkTEyZMQJIkZs2axcMPP4yvr6+rQxMEoQrVJvaUlBTOnTvHq6++ylNPPYVc1iPcYDCwcOFChg4d2mRB1sf+zDxKzdZKid1QcpTc9A8pKbw0r12+ra2ud+oAf2XkAtDPwZcHQWjOnnvuOZYtW4bVauXWW29Fo9Gg0TivfbEgCM5VbWLPzMxk8+bNpKam8s4779iPKxQK7rjjjiYJrr6yS41kl5rwvOwuOiftLXIzPrA/VmlaA7ZtbbVdAV+uyGTmWE4hAVoNUd66hgctCG5q2rRpGAwGhg0b5upQBEGohWoTe/fu3enevTsDBgxw+7vzy31+NAUAtaLySnUZMwDh7TeQ/e63HL3/pnotmAPYfSEHqwzdg33rtCJe9FMX3N3Ro0d5/PHHWbJkCR06dCAyMpKlS5e6OixBEGrJYUlZtVrNxo0bAXj88ccZPnw4W7ZsafTAGqK8o9q4uNZVPu+h60Duf7fWe8EcQK+QVnQK9KZrUM0r7ivaP2c1R5Z8Y+/WJlbBC+7o9OnT7Ny5k2+//dbVoQiCUA8O92e9++67vPfee/z6669YrVY2bNjAww8/zPDhw5sivnrx12rw81AT5lVzOcv67FlXSCBJ4K1RMbpNaJ1eW36nHv/4GLFXXXArx48fJyIiAk9PT0aNGsW2bdvo3r27q8MSBKEeHN6xa7Va/P39+fXXX7npppvQ6/Vu3+ntvo5R3Nz20t16ccEOLpyZQUnBjgZf29NDjUcd96tXJArQCO5m+/btDBgwgIULF9qPiaQuCM2XwwxtMBj46KOP+O2337juuus4e/YsBQUFTRGb01xMWUhu+rsYS44gSVqU6tr3R7+caDwptDQ9e/akR48ebl+bQhCE2nGY2BcsWEB6ejqvvPIKHh4ebN++nSeeeKIpYquXcwUl/O9YCidyCu3HyhfNxXTeS1yPMyhVTbtgbf+c1WyMfYzilOwmfV9BqIrFYuGdd95h8+bNAOh0Or755hvGjBnj4sgEQXAGh2PK7dq145577iExMZGtW7cyePBgwsLCmiK2eikymUnKL6Gd35X9nz10HZEU6ga/h61nXO1VLB0rFssJrpacnMxLL71EXFwcI0eORKFQNKh5kSAI7sVhYv/888/58MMP6dy5M7Is88orrzB9+nRuvvnmpoivxRClYwVXslqt5Ofn4+fnR5s2bVi5ciU9e/Z0+/UygiDUncPEvnHjRr777js8PGw9zYuLi7nvvvuaTWIvyt1Ccd42V4chCC6Tn5/PpEmT0Gg0rF+/HoVCwYgRI1wdliAIjcRhYlepVPakDuDp6Yla3fDh7MZiuqz3embKCwCo1GEgNbyee4nRjKIWS+hEIRrBXXh7e+Pr64tGo6GkpAS9Xu/qkARBaEQOE3toaCgLFiywt2bcvn07rVtXXfjFHVwoKgXAbLUVqUG2ANCm22EkyTbsWLHxS12ZrTKqWkxHVpxXF3PrQlM7e/Ysu3bt4vbbb0eSJJYvX45WqxVz6YJwFXCY2BcsWMCqVatYv349AN26dWPKlCmNHlh9xfrqyTeaifO7dFciKfQold72xw1p/FIXYl5dcAWr1cqkSZM4ffo0ffr0oU2bNuh0op+BIFwtHCZ2g8HAP//5z6aIxSni/PSVkjqAbDRyICHO/tiYmlKvxi8mqxUvrRqr5cpV8RWH3kEMvwtNz2KxoFQqUSgUvPrqq2RlZRETE+PqsARBaGLVLondu3cvN9xwAyNGjGD06NEkJyc3ZVwNcu6ZORxIiOPvu1pTWrQP2WTCmJxkf76+9eHNVhmFJFVZpaZ86L2cGH4XmtLKlSvp378/+fn5AAwYMICJEyeKoXdBuApVe8e+dOlSVqxYQbt27di5cydLlizhjTfeaMrY6uVodgG7g+OIklQE35IDgHxRTejMx+p8h345i7Xm/eti6F1wldTUVNLS0jh69Ch9+vRxdTiCILhQtXfsCoWCdu3aAXDdddeRnd08qqbtSssh7frB0PYaPKJti+M6TMhucFIHMMtlib1u9WkEwelkWWbr1q3IZX8nZ8+ezc6dO0VSFwSh+sR++RBecxjSM1utXCg2AOCRexEAlSYchcI5C4csZb9E61p5ThCc7aWXXuL2229n7dq1AGg0GkJD69ZtUBCElqnaofi8vDx27txpf5yfn1/psTs2jMg1mOz/2zPzArVYG1gnjobi3d2uU5tIzk6s9nmj0cjpPVsxW0zVniO4hylTpnDq1Cn69evn6lAEQXAz1WY+Hx8f3n33Xftjb29v+2NJktwysR/LtjV+8T11tFGur1UpMJotyM00wZ+9+DelxiL0Hr7VniMhoVZq0Gn0hPi2acLohJqkpKTw1FNPMW/ePNq1a0dMTAwrV650dViCILihahP7qlWrmjIOpzBs3gTdbiB8zYpGub6PRk2pyYKqGUxLVMdb68+EXlV359u3bx89e/Zs4oiE2ti/fz+bN2+mbdu2vPDCC64ORxAEN+bcsWoXOffMHLI3rMM7OYnBgCYqGv+bJ2Jko6tDE4R6O3/+PP7+/mi1WsaOHcuGDRu48cYbXR1WJWazGWt5lUc3YzQaXR1Ciyd+xs6hUChQqZyXjltEa6fsDevsRWdCZ/6bromnnLIK/nIXikrRqpUoFM33jl1oHvbs2cP111/PokWL7McGDBjgVotYCwoK3PYXe1xcnOOThAYRP2PnMRqNFBQUOO16zf6OvWLd97TNO8hTqQg2W/BQNbzhy+XyjGY0KiUGk9np1xaEihISEoiJiXHbX55msxmlUomnp6erQ6mSyWRCo9G4OowWTfyMnUej0VBcXIzZbHbKnbvDO/bU1FRmzJhhrw//5Zdfcvbs2Qa/sbNkb1iHDJybu4ij2YXsTMtGrVSQm7Eck+EUyM5b6Ga2in3sQuOQZZk1a9awdetWAPR6PT/99BN33323iyOrmtVqderQoSBc7ZRKpdOmtRwm9vnz53PTTTfZC2HExMTw3HPPOeXNnUXu2pPD13THYLHSWu+BQpLITH4GAI2undPex+LELwmCUNG5c+eYOXMmc+fOxWKxdSRUKFrETJkgCLXgzGk2h785zGYzQ4YMsb9p7969nfbmzpLTLgGA9q28uLNDJAAyFhTKVkTG/+C09ym/YxfpXXAGWZYpKioCICoqinfffZd169ahVDp/GkkQhKuHw7E0k8lEfn6+PbGfOHECg8FQq4u//PLLHDhwAEmSmDt3Ll26dLninCVLlvB///d/Ddped3bEzQDoVUqUZQvbJCSUmtZIkvOGC8Udu+AsxcXFPPTQQxQVFbFu3TokSWLChAmuDqtZSUlJYezYsXTq1AmwLUBq3769fTtgSUkJixYt4uDBg6hUKgIDA3n++ecJCwsDbD3rX375ZbKzs7FarXTv3p0nn3yyVvPGX3/9NW+//TYLFy6kV69e1Z63a9cuVq9ezZtvvtnwD1xm//79LF68GJVKhUaj4bXXXsPfv/pOkunp6QwcOJC33nqLoUOHArB+/XpOnDjBk08+aT/vqaeeYsSIEQwaNIisrCxeeuklkpOTUSgUREdHM2/ePHx8fOoUa0FBAY8//jgFBQV4enqyZMkS/Pz87M//8ssvfPzxx/bHhw8f5rvvvmPZsmUcP34csP05+vj4sHz58jq999XM4R37tGnTuO222zh8+DBjx47lvvvuY9asWQ4vvHv3bpKSklizZg0vvfQSCxYsuOKckydPsmfPnvpFXsF1C2ZxU1wog6MCG3ytmmgUElarLG7ZhQbT6XQYDAZMJpNTV8Nebdq0acOqVatYtWoVa9aswWQy8fXXXwOwaNEigoOD+eqrr1i7di1Tp07lwQcfxGQyYbFYeOyxx5g6dSpr165l3bp1ALzzzju1et8dO3Ywe/bsGpN6Y1mxYgWLFy9m1apVdO/enS+++KLG87/55huio6P59ttva/0ec+bMYciQIaxfv561a9cSHx/P/Pnz6xzrJ598Qp8+ffj8888ZPHgwH374YaXnBw4caP/ze+mll+jbty8hISE899xz9uMDBw7k1ltvrfN7X80c3s727duXr776iuPHj6PRaGjTpg0eHh4OL7xz5077t8O2bduSn59PYWEhXl5e9nNeeeUVZs2axdtvv92Aj2DTwd+7yuMpBSXsS891Si42W61YZBmruHMX6iEzM5M///yTsLAwJEnio48+wsvLq0XMpc/5eh9rDyQ5PrEObukazeKxdSuY1KVLF5KSkigqKuL333+3L0YE6NWrF126dGHbtm14enoSGxtrb5ojSRKzZ8++4s/CZDLx/PPPc+7cOYxGIzNmzECSJH777TcOHTqEj49PpcY7Cxcu5ODBgygUiisS4fLly/nhhx+wWq0MGDCA6dOnk5iYyPz589FoNGg0GpYuXUpKSsoVxyreKZff/cuyTHp6usOiUt988w3PP/88s2bNori42OFOhlOnTpGfn8/YsWPtx+677z5KS0srnff777+zevXqSsduu+22Sq/buXMnL7/8MgBDhw7lkUceqfZ933rrLaZPn17pWHlp82nTptUYs1CZw8ReXavWmTNn1vi6rKwsOnbsaH8cEBBAZmamPbGvX7+ePn36EB4eXutgDx06dMUxkwzHHvgXp/f+TZR0aU+tp9mMyVzC9qOnScN5WzLUSgUXcwrZt29fpeOGsv28lx93JyajCYsk1xijO8ffnMmyzCOPPEJSUhLLli1zdThOERcXh8lk6ytgMpnsC2ydxWQy2dcgVKWkpASr1Wo/x2QysWXLFm655RZSUlKIiorCYDBUmjqMi4vj2LFj6HQ64uLirri+xWKxfyawJUWFQsEHH3xAZmYmU6dOZePGjVx33XUMGTKEjh072q+xa9cuzp07x/Lly9m3bx8bN26kT58+mM1mioqKMBqNfPjhhygUCsaOHcutt97KmjVrmDBhAmPGjGH37t0kJyfz5ZdfXnGsTZvK5Z3/+OMPXnvtNdq0acOQIUOq/TmdPXuW/Px8unbtSs+ePfnuu+8YOXKkfbSo4uvMZjOlpaUcOXKEtm3bVnnNisf69+9P//79azwnIyMDDw8PioqK0Ol0pKenV3ndzMxMMjIyiI6OrvT8Z599xujRoykuLq7y87UkJpOJU6dOOeVaDhN7xYU8JpOJPXv2kJCQ4PDCl/8jl2XZPk+fm5vL+vXrWbFiBenp6bUOtlOnTleMFuzT6bgwYCR6Hz0924fZj5/Yq0Kp1uHvHUDaxQLu7RiFt7p+i5K2JmVyNKeQ7kE+3LLsJwbEhtDzH4MrnZNSNi/nziVZT+7+HrXCo9oYRUlZ56v4937RokWcPHmS8PDwZv9zLi9MUz4fvXRCX5Y28RIBnU5HUlISDz/8MADHjh1j6tSpjBkzhr/++gtJktDr9ZVeo1ar0el0aDQaTCbTFc9f7sSJE/Tr1w+9Xo9er0etVmMymVCpVGi12kqvP3XqFH369EGv13PjjTdy4403smvXLlQqFXq9Hh8fHx566CFUKhW5ubmYTCZGjhzJCy+8QFpaGqNGjaJ9+/YUFBRccexyw4cPZ9iwYfznP/9h9erV9p/B5X788UfGjBmDXq9n/PjxbNiwgYkTJ+Lh4YFara4Uv0qlQqfTYTabUSgUDn82RUVFDs8p/zPQ6/U1Xvezzz5j/PjxVzy3ZcsW1qxZg1arrfF9WgKj0Ujnzp0rrfEwGAxV3tA64jCxXz40Uj435UhISAhZWVn2xxkZGQQG2ubA//zzT7Kzs7nzzjsxGo0kJyfz8ssvM3fu3LrGT0lAsC0uB3cLOqUCT3XdF9Il5RdzNKcQf62a3iF+FBlEcRqhdjZt2sRbb73Fhg0b8PLyYujQoQwdOlSMijhR+Rw7wIwZM+x3thEREZw5cwaj0VjpF+XRo0cZOnQoGo3mimFko9HI2bNnr0ikFW9SrFZrtVMnNe1DTk1NZeXKlWzYsAG9Xs+YMWMAW5fMtWvX8vPPP/PUU08xZ86cKo/17dvXfq2tW7cybNgwJElixIgRvPXWW9X+fDZv3owkSfzyyy9YrVbOnTtHfn4+/v7+5OfnVzo3OzuboKAgdDpdlSO1hw4dsi9UhNoNxYeEhJCZmYm3tzfp6ekEBQVVGecvv/zC0qVLKx1LSkqiVatWV0VSd7Y6T+5ZLBaSk5MdntevXz9++MG21SwxMZHg4GD7MPzIkSPZvHkzX3zxBW+//TYdO3asV1IHODP6NgCUTi61eTS7gM+PprDmWCoAo9uEom4Bc6FC0zlw4ACHDx8WibyJzJ49m//85z+UlJSg1+sZNGhQpfU7f/31F4mJiQwcOJB+/fqRmprKTz/9BNgS9muvvcbmzZsrXbNz587s2rULgLS0NBQKRbUrwyueWz53Xi4nJwd/f3/0ej2HDx8mNTUVk8nEZ599Rm5uLuPGjeOee+7hyJEjVR6r6K233rIfO3DgwBXD9OUOHjyIXq/n+++/Z+PGjXz99df84x//YMuWLXTp0oV9+/aRnZ0N2IbsU1JSaNeuHbGxsYSGhlZK2itWrOCTTz6pdP3+/fvbF7iV/1cxqYMtD3z//feA7e67qqF7sNVxCA0NvSL+Dh06VHm+UDOHt7CX16fOy8vj5ptvdnjhHj160LFjRyZNmoQkScybN4/169fj7e3NsGHDGhZ1BRfjbVvoOgVWvXiutmRZpshkwUujwmy1suN8NpklRsK9tPQM8SPMS0uhQfQpF2q2Z88eevXqhSRJzJkzh8mTJ7ttWdiWJjIykhEjRvDee+/x0EMPMXfuXJYsWcK4cePQaDQEBQWxbNky+/Tixx9/zPPPP8/bb7+NRqPh+uuvv2KEcvTo0ezevZspU6ZgMpl48cUXq33/3r17s23bNiZPngzAvHnzyM3NBSA+Ph69Xs+kSZPo2bMnkyZNYv78+dx///3MnDkTb29vNBoNixYtIjEx8YpjFS1cuJD58+ejVCrRarUsXmzrizFr1iwWLVpkv8P95ptvrthCOXHiRN555x1uueUWnnvuOaZPn45SqUSlUvHaa69dmlpZupQXX3yRL7/8Eq1WS3x8PC+99FKd/0ymTJnC7NmzmTx5Mj4+Prz22mv2z3D33XcTGRlJTk4O3t5X/v7OzMyscRufUD1JdrDiJTU19dLJkoSXl1ed9zI2VPk8Q1Vz7O+t+Zb82PY82btyhbkTe0NQqkNJ9P6BwxcLeKRLDD4e6iqvb7ZaWXv8PBdLjTzcpQ1KhYTJaiW31ESQ56X3KzSY8J37P0bFh/P11Mpz7BtjbdMTN52ufljM1dbsXoha4SHatjaSpUuXsmDBAj766KMa96S3hJ/z5XPs7qY2878tzZIlS5g5c2aTlfq9Gn/Gjamqf1M15b6aOPwb8Nprr/H//t//q0eYTeZvwQYAACAASURBVKPn688hK5Wwo+774WVZZl9GHrvTcigwmWmt96DUYkGvUKFWKCol9ebGarWQU5xOxU33Vqu1hfTzc0833XQTv/76a6XdIILQVLp37y7q9wtALRJ7REQEa9eupXv37pW+SURGRjZqYLXlUZBX79cezMpnW3ImAF5qFaPahKCvxwI7d7Tz1AZOpO+94rhOI75hO0tOTo59f3BsbCyxsbF89dVXrg5LuEoNHjzY8UnCVcFhFrt8MQnYhuS3bdvWKAHVxdm8YnY/9Spxmz6v82tP5xWxJSkDD6WCuxMi8de655BifRUbbdXMOrS+DoV06TY9vNU1rgqpxfntt99YvXo1Wq3WPncoCILgatUm9k2bNjFu3Dj7qlF3dCS7gOz4rgTv21Hn17bx8WRsbCh6tarFJfWKesX8A5Wy5X6+ppabm4tWq0Wr1TJu3DhWrlzJqFGjXB2WIAiCXbUzrmvXrm3KOOolpbAEAJ/k03V+rSRJdPD3JtJb5+ywhBbq0KFD9OvXz74KWZIkxo0bJ+Y1BUFwK816KZWPxrbK3SfpZJ1eV2A0c76wFIPZ0uAY9s9ZzcbYxyhOyW7wtQT3FhMT45JdIYIgCHVR7a3G/v37GThw4BXHy0tk/vLLL40YVu2klt2xK8x1qwb3d1Y+v6de5Lb2YbTxbdhisuS1f1Kcko1nhD9Rt/R1/AKhWfnxxx9RqVQMHDgQLy8vfv/9d7fd4nW1uVrbtpb7/fffmTp1KseOHavxvObYtlWv1zN79mwKCgqwWq0sWLBA1IOog2oTe0JCAq+//npTxlJnCkkCZCRL3e685Qb2euu2dhcbX94AYE/q7rx/Xaif8+fPc+eddxIWFsaePXvs/a8F91GxpCzYktPXX3/NsGHDKrVtBdi7dy8PPvggX331FQqFgscee4znnnuOPn36IMsyL730Eu+8806t2lK7sm0r2PY3L1u2rNoSrRVVbNtantgdmTNnDjfffLO9ktxHH33E/PnzWbJkSZ3iLG/bOnXqVFavXs2HH37I7Nmz7c8PHDjQfgOZlJTEq6++SkhICG+++SY9evTgwQcf5JdffuHNN9+stiGZcKVqE7tGo6lT5zVXmNC2NSem3oMkV12fuTpphbZuT37VFKxxJPKv0xTnFuMZ4S/u1Fsgk8mEWq0mLCyM119/na5du4p5dAf2nNnM2ayDTr1mTGAXerep28LEq6FtK8D777/P5MmTa7Ubozm2bX3ooYfsFU/9/f3tFfyE2qn2t1WXLl2aMo56ifLxJOfQX3V6TaHRzNn8YoJ0Glo1YDW8uEtveQwGA08++SRpaWn873//Q5Ik7rzzTleHJdSSyWRi27Zt3HHHHaSkpBAbG3vFF7L4+HjOnDmDTqcjPj6+0nNVNRv59ttv0Wg0fPbZZ6SnpzNlyhR7zfMRI0ZUSuo7duwgLS2NNWvWsGfPHjZv3sx1111X6Xr//e9/USgUDBkyhHvvvZf169dzxx13MH78eHbu3ElmZmaVxyom9jNnznD06FFmzpzpMLGfPn2agoICrr/+eq699lp++uknewOa6pw5c+aKn41Sqbyiylz//v0ZOXJkjdfKysqyl4UNDAwkIyOjyvPS09O5ePGivXNoxSprn3zyicOYhcqqTewVh0vc0f9l5FFitiCHhKFPP1+r1+SWmvjoUBIWWSbev2G15YWWR6PRcO7cOdLT0+1NO4Ta6d1mVJ3vrp3hzJkzTJkyBbjUtnXo0KH89ddfWKqYopNl2V4rvqrnL3fo0CGuvfZawNapTKlUVnv3ePjwYXr06AHY6sb37t3b3hQGbF8c7rrrLlQqFTk5OeTm5jJkyBBeeOEFzp49y6hRo4iLi6vyWEWLFi3i2WefrcVPx7YWYPTo0QCMGTOGDRs21Jgky++Sa/OzqY2KFcsrtjC+3FdffXVFAxnAXr/+1ltvdUo8V4tmO764Iy2bAqOZbpFtap3YfT1UtGulJ9xLR49g30aOUGgOCgoK2L17N0OGDEGSJJYtW2ZvviG4v6utbWt6ejqnT5/miSds/R4yMjK46667+Oyzz6p83+bctvWNN94gOzubhQsXVvkaoXrNdrtbeZvWoINXlk2tyGSx/UMzWCxIksRNca3pFeJXtvCubg7P/ZzXP/4Zz5yiugcsuB1Zlrnlllu488477SuLAwICRFJvpq6Gtq0hISH8+OOPfPHFF3zxxRcEBwdXm9Sbc9vWvXv3cvDgQRYuXFjtFymhes3qjn3tgSQ++SsJgCE9YzBbrGRdtA2LDX9/a6VzX+5kIr+gkL052fjoPXj6hwMkp+dfcc1yvdbvJmb/2Rrf3yu7kCCgMMCLjmLBXLMnSRKzZs1i3759xMTEuDocoYGulrat1WlJbVs///xz0tLSuOeeewDw9fWt9CVNqJnDtq3uoLx13dw/M/nxdCZKhcSC22zzXvFjbwDg5jv/U+k128c9RlapL9s13+DvpeW9rYc4d7Gw2vd4/eOf8S8sJdvrygU0Fe1uH8o1C27n+RFdG/ipGtfWwytIzTnGXde9WOuSsi2hnagj27dvZ+nSpXz66acuaznZEn7Oom2r+xFtW5u3Jm3b6m68PFQkPj2ez46m4K9VE+3vBYBhceXVy6f3P0GIny/n8KLAaGb7YyMJvawN64Gn/su5dbahs5JiA7qoAO44UfNeyWmASimGhpqrrVu38uuvv7J9+3ZGjBjh6nAEwWlE21ahXLP7WyAhEejpwS3twvDWqEgrO355spUq/H8AjUK64pyUdbsoKSswU74fXSTtlufo0aN06NABgKeffpqbb76Zbt26uTgqQXAu0bZVKNfsEjuAh1JBnJ9tCCjNwbmUzTRUt81C7Edv2T766COefPJJli9fzk033YRWqxVJXRCEFq1ZJvaapJ/9F6VFB7CYc1GqQym/a1eU5fX9c1aTvPZP4FI5WKHlGjBgAJ06dbLXBxcEQWjpmmViTysq5b9HU+gd4kfF3ehWSyE5Fy6tnNR59+X/t3fnYVGV///HnwPMyCIgCIILbril5pJp7hu4a+pHCkhw33DBzJ8GrhSaaaYpkplLi0u5RG4pWoapuYsKaqaiIi6JgIisMzDz+4PvnJjYFRwG7sd1dXUx58w599wq7znb+1UbM67GPyesawDy+GRSouMAsKhjV27bwSamxvLgacHBEOVVamoqS5cuZfTo0dStW5eGDRty9OjRfM/YCIIglDcGWdg1GshUa8jK535+iyr9cWqyN/uH2/8AkPYoEbnCBIs6dtR2a0/rZeW3VWhETPazuTJkGMmM9TyaV+u3334jKCiIuLg4goODgfwvwwiCIJRHBlnYU4sR05r+f5nrZtWrMPhc0Z4HNXRZ6uz5GdhqKkZG5b+wp6WlYWxsjEKhYNCgQaxatYphw4bpe1hCKauosa2BgYFcvHhRetRs7NixeUZsa5Xl2NasrCwWLFjA3bt3UalUvPfeewwZMoSbN29KPQOMjY0JDAzEycmpWPuuyAzyFvCwmOzT6RlF6Gf8MCW90HXKK4tKVQpfycDdunWL7t27S40vZDIZ3t7ehSZYCeWDtqXs5s2b2b59OyqVin379gHoxLbu2rWLcePGMX78eFQqFVlZWUybNo1x48axa9cufvrpJwDpLE9h9BnbmpqayuLFi6XPXVBRB93Y1qKaPXs2Li4uhISEsGvXLl577bVcaXVFoY1t/eGHH+jZsyfr16/XWX7s2DHS0tLYunUr33//PcuXL0etVhMUFMSECRPYvHkzw4YNy/U+oWAGd8Q+/sQ2EoZmN/ew8RyI8sF9FDVrodGoeRg1WmddjUaDusy33xFehoODA0qlMlekpPBqxcydTcLPP5XoNm2HDsNp8bJivacixLampBSvpXVZjm21sbEhKSkJtVpNamoqFhYWGBkZUaVKFalrX1JSEjY2NsX6zBWdwRX2rjfP8ij8FDJTM0yfJUDNWtgOHUam8iHJCT8DYGaZHZX4270nZGQVL6tdKPsuXLhARkYGHTt2xNLSkuPHj1O5cmV9D0vQs4oS25qSksKaNWtISkrCwcGBefPm6Zzezqmsx7a2atWKGjVq4OLiQnJysvQlYPr06bi5ufHll1+SlZXFrl27CtyPoMvgCjvAW6s+ouW1KBj5b5SfKuM+AFZ272FX05+Y52mExz6jkrGRKO7lSGxsLAMHDsTe3p5z585RqVIlUdTLAKfFy4p9dF0SKmJsq4eHBw0aNKBevXqsXbuWoKAg5s+fn+eYynps6/nz53n06BG//vor8fHxjBgxgm7durFixQpmzJjB22+/zZYtWwgODsbf379ExlQRGNQ1diMj+Gf0ZO537lXour/fewJADYuCe78LhkEbh1mtWjUCAwMJDg4uVu9koXzKeY29ffv2eca25nT9+nWcnZ1xdnYmMjJSZ5lSqeTGjRu59lGSsa0bNmxg8+bN1KxZE/g3trV+/fr4+flx+vTpPF/LqVevXtLn7NWrl5RMmJcDBw5w6NAhBg8eTFBQEKdOnSo0trV+/fq55gayv+TkdPz4cby9vXX+097foKWNbQXyjG0NDw+nQ4cOmJiY4ODgQJUqVXj8+DHh4eFSElzHjh1z7VsomIEVdhkJQz150rrwZ89fq2pJ40vRxO8PfwUjE0pLZmYmixcvZtSoUdIv2HHjxuUb/yhUXBUhthVg0qRJPHz4EMi+675hw4Z5jscQYlvr1KnD5cuXAUhOTpaKf506dYiIiAAgMjKSOnXq5PkZhbwZ1Kl4uXHRH91q52jDniV7yBzdDYCa/d8orWEJpcjIyIhz585x584dYmNjcXBw0PeQhDKqosS2enl5MW3aNMzNzTEzM5OWG2Jsa69evfjzzz/x9PRErVYza9YsTE1NmTVrFgEBAWzYsAGFQkFgYGCx912RGVRs6+obKTRtUJ0qN64ycfgQnXVUGfeJulgXK7v3qNHgey7O3spfn+/n2bL3SOraBJ8WdbGqJNfTJ3i1wv7aQnT8FTzemo+pvOiximUlTlSpVBIeHk779tlnZv755x8sLCxyZTYbqrIyzy9DxLaWPSK21bCVZGyrQZ2KtzLP/sDWdwpulxqfpuREY0eSB7TGoo7dqxiaUII8PT0ZOnSodO3Q0dGx3BR1QSgtIrZV0DKovwXWFtnfWGz+vlrgeokZKlJb1Mbs70fYvlGPR/HPX8XwhBIyZswYnJycqF69ur6HIggGQ8S2CloGdcR+82Eitj//QNW/Lhe43rnHTwGwuBLzKoYlvKQrV64watQo0tLSgOzrml988UWx21cKgiAIBlbYE59n4PjNl8hTkwHIVMVy71ov7kS0JeZ69rOaD1X1iU5Kw+JyNObXH+pzuEIR/fjjj+zdu1e6e1YQBEF4cQZ1Kl4GaHKEmqQnnyU1KQwAI2NLko1bcirVDSMZ2G87qadRCkVx//59atWqBcCcOXNwdXUttOe1IAiCUDiDOmLv1Kwmf2/Zl+t1+9pLadT2KXes96IxMqenkz2md5/oYYRCUWzfvp02bdpIoRTm5uaiqAuCIJQQgzpiL8w7jWqizFKjMDbivr4HI+SrVatW1KxZEzMzM30PRTBgFTW2VaVS4efnR3R0NBYWFqxevRpra+t81zfE2NbC3icUzKCO2ItCYVzuPpLBy8zMZPXq1cTEZN/M2LhxY86ePSvu4hVeWkWMbd2xYwc2Njbs2rWL/v37c/78+QLXN8TY1sLeJxTMYI/Ylel3uP/3v01qVFlqbiQmY1NJQY3Kht8fXq1R88f1rSSlxRf7vckZT0thRC/u0KFDBAQEEBERwYYNGwDE87blzMXZW7m363ThKxZDbbf2tF42vFjvqQixrWFhYfj6+gLg7u5e6JwYYmxrYe8TCmawv13Tnv97c5xZ5bdIyFCx//ZjWttbl4vCnq5MJjr+KkYyY0yMi9fdSyYzoppVHRQm+jvVnZWVhVqtRi6X079/fxYtWoSnp6fexiOUfxUltvXBgwecO3eOVatWYWVlxcKFC8tdbGth7xMKZlCF3TIlkaz/XJd1rL8Oc6vOXHqYHWZQ1axstrh8UXXsmtOtsWEVxPv37zN69GhcXV358MMPkclkTJ48Wd/DEkpR62XDi310XRIqYmyrRqOhevXqbNy4kS+//JJ169bpXCvPyVBjWwt7n1AwgyrsNQ78hEnyM2yHDtN5/cCdx0TGJWGcmcWdAUu5n5BM6v0EzGvZ6mmkFZuVlRWPHj3i3r174h+lUKq019gBfH1984xtzXkz3PXr13F1dUWhUOQ6jaxUKrl79y6NGjXSeb0kY1t//vlnLCwspOKqjWgNCwvDz8+P2bNn5/maNjcBso9gtdf2O3fuTFBQUL7zc+DAAWQyGUePHkWtVhMTE1NobKuZmRmrVq3KtS1tz3KtopyK18a2WlpaFiu2tbD3CQUzqDvNqpw6ivzwQZwWL9N5/XpCdstYx5UHUUXcA8C8li213QqPdxVKxq1btzh37hyQXdiPHj1KcHCwKOrCK1NRYlu7du3K8ePHgeyzBNovM/9lyLGthb1PKJhBHbHnRa0BlVqDk6UZFudvQy1bBt/+9xvsg9v/6HF0FcPTp0/p2bMnNjY2nD59GjMzM+zsRPiO8GpVlNhWb29v5s6dy+7du1EoFCxduhQoX7Gt+b1PKBqDim298scZkhWmTJk6hmdPtvIoaiQO9daRaeWFETJONJsJoFPY99/+h6vxzw0utjU1I4kd5z6hnn3LV3aN/WXiRIOCgqhduzaDBw8u4VGVPyK2tfRVxEhREdtq2EoyttWgjtjT6jqTaqN7rUUmA3uzon9g4eWp1Wq+/vprLl68yFdffYVMJmPatGn6HpYgVGgitlXQMqhr7HlRayBTrcYATjyUK7/88gtHjhzhwYMH+h6KIAiI2FbhXwb99U6FBd/EdCLzXhS2e85TWdwJX2rUajVXr17l9ddfx8jIiLVr16JQKKhWrZq+hyYIgiDkYNBH7M9pQKYm+0YYk9+uijvhS9HYsWPp06cPt27dArIfJxJFXRAEoewx6CN2zf99L6kacg7b2Gc6N80JJWvIkCGoVKpih0AIgiAIr1apHrF/8sknuLu74+HhQUREhM6y06dP8+677+Lh4YG/v3++jR1yMrt7C4srl6SfNdrvJUV4r1A89+7dY/r06VJ/6MGDB7N582ZxlC4IglDGldoR+9mzZ4mOjmb79u3cunULf39/du7cKS1fsGAB33//PY6Ojvj6+nL8+HG6detW4Darb1lPxqN/YNoYADRkn4aXZYnCXtK+/vprNm/eTLt27Rg+PLtVqGg2I5QlFTW21dfXl6dPs4OeEhMTadWqFYGBgfmub4ixrTdv3pR6BhgbGxMYGIiTk1Ox9l2RldoR+6lTp6S/RA0aNCApKYnk5GRpeUhICI6OjgDY2tpKf1GLw5wY2la5g/k1cWd2ScjZYtLf35+NGzdKzTYEoSyqiLGtq1evlj5z8+bNeeeddwpc3xBjW4OCgpgwYQKbN29m2LBhIra1mErtiD0uLo5mzZpJP1etWpUnT55QuXJlAOn/sbGxnDx5kunTpxe6zZgJH5DQqCkPLt9BrW5JltG3GD+3I31KLwC+unwn13tSM0smzKC8++WXX5g4cSIbN26kT58+WFhYMHToUH0PSzAQsdGzSYr/qUS3aVV1GNXqLCt8xRwqQmyrlja5rUWLFgXOiSHGtlapUkXq2peUlISNjU2BYxZ0lVph/+9z5XmFgcTHxzNp0iQWLFhQpD+4lNdboVaYkaFUoiYLDRaoszJRk73djP/r3JOTMVAZNX9HRmBkQGeSVZo0IDuY4cKFC6W+v7S0NBQKBVevXhXtYF+BV/FnWtqcnZ1RqVRAdgEs6V4SKpWKlJSUfJenpaWhVquldVQqFYcPH8bNzY379+9Tu3ZtMjIyyMjI0Bnz33//jZmZGc7Ozrm2n5WVJX0myC6KRkZGrFu3jidPnjBu3Dj27NlDhw4dcHFxoVmzZtI2zpw5Q0xMDJs2beLChQvs2bOHdu3akZmZSUpKCkqlkvXr12NkZMSgQYN455132L59O//73/8YOHAgZ8+e5d69e+zcuTPXa3n1g9+0aRNubm4FztHdu3dJSkqiZcuWtGnThoMHD9K3b18yMjJyzW9mZibp6en89ddfNGjQIM/t5nytS5cuefZwz7lObGwslSpVIiUlBTMzMx4/fqyzvGHDhtjb29OjRw9SUlJYuHAhKSkpjB8/Hi8vL9asWUNWVhZbtmwp8HOWByqViqioqBLZVqkVdgcHB+Li4qSfY2NjdQpGcnIy48ePZ/r06XTu3LnoG9ZomN6uMUdv/s65pw4McrzCNfcTAOXqrvjUjCSun9uPra0tbRqXfPtRjUbD1q1b6d69O7Vq1aJNmzbUqFGDjh07lvi+BF3lsaWsRYOVwMpXOgYzMzOio6OZNGkS8G9s68CBAwkPD0cmk+VqeSqXyzEzM0OhUKBSqQptiXrz5k06deqEhYUFFhYWyOVyVCoVJiYmmJqa6rw/KiqKdu3aYWFhQdeuXenatStnzpzBxMQECwsLrKysmDhxIiYmJiQmJqJSqejbty8BAQE8evSI/v3706hRI54/f57rtf9SKpVcvny50P7tv/32GwMHDsTCwoIhQ4bw888/M2zYMCpVqoRcLtcZv4mJCWZmZmRmZmJkZFTo3BSlpaz2z8DCwiLP7Z4/f564uDiOHDkixbb26dOHr776ipkzZ/L222+zZcsWvvnmG/z9/Qvcl6FTKpW8/vrrebaULa5SK+ydOnUiKCgIDw8Prl27RrVq1aTT7wCffvopI0eOLPSGufw8VZmilimQG+n3VHtGZirKzLQS326aMrnwlV7Cr7/+iq+vL4MGDZJSm4rTi1gQyoKKGNsKcO7cuUJPwYPhxraGh4fz//7f/wOgY8eOzJ8/v9DPKvyr1Ar7G2+8QbNmzfDw8EAmk7Fw4UJCQkKwtLSkc+fO7N69m+joaHbt2gXAwIEDcXd3L/L2E1VmyDRKrExKvqgWVaoyiV3nlqLWlN6XC1kJ3t+o0WhQq9UYGxvTq1cvZs+ejZeXV4ltXxD0adasWYwbN47OnTvrxLZ+8MEHwL+xrYsWLUImk7Fs2TJ+//13evbsKcW2WlhY6BR2bRTrgAEDihTb+vXXXzNu3DiuXbvGzp076du3L1BwbGu3bt14++230Wg0/PXXX9y6dSvXa/8t7JGRkTRp0qTA+dDGtoaEhEiv+fv7c/jwYXr27Mmnn35KQkICtra2OrGtCoVCim3VPhHzzTffcO3aNZ2UtS5dukifLz/a+NXJkyfnG9uqjcrNGdtap04dIiIi6NatG5GRkdSpU6fA/Qi6SrVBjfYbl1bOv4gvcnpBS63R8FRlRmXu6PW6eZryOWpNFlXMq2FXueQfxZDJZDSuXjKd9GJjY5k2bRrt2rVj5syZyGQy/Pz8SmTbglAWVJTYVoAnT55Qu3ZtndfKU2zrrFmzCAgIYMOGDSgUigIf5xNyM6jY1j/T5aTJTenmZMcf9+Opp95K73pO/NE+u2nNq77GHp/8gH2XgmhaoxPt6g8q/A16lJiYSKdOnXj99df54Ycf8nwmvTxc+zUE5WGeRWxr2SNiWw1bSca2GlSveMXD+5CexqPkdMyMlDTRlJ+b5UrDo0ePpI5/VapU4dChQ/kWdUEQDJuIbRW0DOpvgez//hvSoDoP//mT59HP9D2kMispKYmuXbtSuXJlTpw4gYWFBbVq1dL3sARBKCUitlXQMqjCrnSsgUZmjEwmo7KJkuf6HlAZZmVlxYQJE6hatSpmZmb6Ho4gCILwihhUYVebmkGmmlP3IjB9+CGiXP1Lo9Hw008/8eeff7JyZfbzxLNmzdLzqARBEIRXzaCusWsde2xGGg4AmFl20vNoygaNRsPGjRvZuXMnd+/e1fdwBEEQBD0xqCN2gP/rHospT6jRcDuVzAp+lrM802g03L17l3r16mFkZMTatWvRaDTUrVtX30MTBEEQ9MTwCrtx9pArEa/ngejfBx98wM6dOzl+/Dj16tUTBV2oUCpqbOu5c+dYsWIFJiYmmJubs2zZMqytrfNdvyzHth49epSNGzdKP1+9epWDBw9iZGSEn58f6enpVK1alSVLlohH64rBIE/FW5mkYExG4SuWc507d6ZFixb5trgUhPKuIsa2LlmyhMWLF7N582Zat27N9u3bC1y/LMe2du/eXfrzW7RoEe3bt8fBwYF169bh4uLC1q1b6dmzp9Q2WCgawzpiV6lAXolhDqdJuKfvwbx6cXFxrF69mrlz51KpUiX+97//MXToUFHYBb0Li3nC9YSSzTdoYluZHk72ha+YQ0WIbbWxsZE62j179oz69esXOCdlObY1p6CgIKnzX3R0NEOGDAGyW9e+//77UtiPUDiDKuyVHtwj3aGm3oNf9GX16tWsWbOGOnXqMHbsWGQymWg2Iwj/R6VSceTIETw9Pbl//z7169fP1bDltdde486dO5iZmfHaa6/pLNO2Ys3pl19+QaFQsGXLFh4/foy3t7fU87xPnz46Rf3kyZM8evSI7du3c+7cOQ4cOECHDh10trdt2zaMjIxwcXFh1KhRhISE4OnpyZAhQzh16hRPnjzJ87Wchd3f3x9vb2+srKywtrZm5syZ+c6JNrO9Y8eOvPXWW/z+++9SAE1+7ty5k2tujI2Nc50KL0qv+Li4OGxtbQGws7MjNjY2z/UeP35MfHw8TZs2BaBRo0YcPXqU5s2bc/z4ceLjxaXX4jCowg4yKloZy9m2cfbs2dStW5eRI0fqeVSCoKuHk32xj65Lwp07d/D29gb+jW11dXUlPDycrKzcBwAajUbqFZ/X8v+6cuUKb731FpCdVGZsbCwdLf/X1atXeeONN4DsvvFt27blzJkz0nJTU1O8vLwwMTHh6dOnJCYm4uLiQkBAAHfv3qV///44Ozvn+VpO5/+X6gAAIABJREFUixYtYs2aNbRp04alS5eybds2RowYkeeY9u3bx4ABA4DsoK2ff/65wMKuPVAoytwURc6O5RqNJt8Dkd27d+sc6U+cOJGAgAC8vLzo1q0bBtD5vEwxqHO4GbVqo6lAR6h//PEHbdq04bfffgOgcuXKjBkzRvrFJAgVXc5r7O3bt88ztjWn69ev4+zsjLOzM5GRkTrLlEolN27cyLWPkoxt3bBhA5s3b6ZmzZrAv7Gt9evXx8/Pj9OnT+f5Wk5///23lDXQsWPHAgO1Dhw4wKFDhxg8eDBBQUGcOnWq0NjW+vXr55obyB3cdfz4cby9vXX+097foKWNbQXyjG3VOnr0KB07dpR+trKyYsWKFWzZsgUXFxfphkehaAyqsGNkBJVyny4rr6pWrUpqaioPHjzQ91AEocybNWsWy5cvJy0tTSe2VUsb29q9e3c6derEgwcP+P333wGk2FZthKiWNrYVKFJsq3Zd7bVzrYJiWxMTE3n77bcZOXIkf/31V56v5WRnZ8etW7cACow01ca2hoaGsmfPHvbt20e/fv04fPgwLVq04MKFCyQkJADoxLbWr19fim3V+uabb/juu+90tt+lSxfpS5X2v5xH3fBvbCuQZ2yrVkxMDI6OjtLPO3bs4IcffgCy7+AX7XKLx8BOxWsADY/vTtf3QEpNaGgoLVq0oEaNGjRv3pzIyMgCH2URBCFbRYlt/eijj5g3bx5yuRxra2vp5jRDjG11cnLi6dOnWFpa6rzPxcWFadOmsW/fPho0aMCMGTOKve+KzKBiW09kmpMuM2Kwuhkgw/mNu8gVNdlTfxpg+LGtx44dY8iQIQwYMEAvj3eUhzhRQ1Ae5lnEtpY9IrbVsJVkbKuBHbH/y+m1UOSKmi/03ozMNG7HXiJLrXqpMaQqkwpfqQi0N5V06dIFHx8fvLy8SmS7giBUHCK2VdCqkH8Lbj2+wLk7+0tsewqTF4ujSUpKwt/fn0aNGjF9+nRkMhmLFy8usXEJglBxiOvQgpZhFXa1Goxf/n4/7ZF6q9quVLV4ubstZUbGOFoV3CAiP5mZmRw5coSoqCjpWpcgCIIgvAyDKuyVYh+RUc2BknqY3d6yNjVtGpXMxoooKSmJhw8f0qRJE2xtbdm7dy/16tUTRV0QBEEoEQZV2GVKJdl3xhumlJQUunTpglwu59ixY5ibm9Oo0av9YiEIgiCUbwZV2J/VrEOWzHALu4WFBW5ublSqVAm5XK7v4QhCufbkyROCgoIKfEStvPD29pb6wGtvxl24cCENGjQAsjvQffPNN8jlclQqFRMnTqRPnz5A9iXBL774ghMnTmBmZoZcLmfu3Lk0btxYnx9Jx/z584mMjGT37t3Saz179mTfvn3Snfn379/H19eXkJAQILub3ffff49CoSAzM5Nx48YV2gI3L3v37uW7777DyMgId3d33NzcdJZHRUWxYMECZDIZdevWJSAgABMTE4KDgzl27BgajYbu3bszefLkl5iB4jGowi43MUaNDPJu7lQmhYWFceTIEekZ0Pnz5+t5RIJQMdjb21eIoq61ZMkS6QzgmTNnCAwM5LvvvuPSpUt8++23bNq0iSpVqpCcnMz48eOxsrKiQ4cObNiwgaSkJH7++WdkMhnh4eFMnTqVgwcPlom77FUqFWFhYSgUCqKionK12M3LhQsX2Lp1K99++y1WVlbEx8fj4eFBo0aNCg3NySk1NZXg4GB27dqFXC5nyJAhuLq66kTPLl++nAkTJtCtWzeCg4M5ePAgrVu35u+//2b79u1kZWXRr18/hg0bhoODwwvNQXHp/0+tGAytmaxGo+HTTz/l4sWLjBgxQpx2F4QSFBISwrlz53j69Ck3b95kxowZ7N+/n6ioKAIDA6lVq5Z0BPfnn3+yYsUKjI2N6d+/P6NGjaJ379507dqVqlWrMnToUObMmYNKpZKeTnFyctLZ38mTJ1m1ahVyuRwrKyu++OIL3n//fUaPHk3btm1JT0+nf//+/Prrr6xevZrz58+TlZWFl5cXAwcOxM/PD7lcTmJiIkuWLGHmzJmkpqaSnp7O/PnzadGiBbt372bjxo3UqFEDBwcHWrVqxeDBg5k/fz4xMTFkZmbi6+ubK1zmv1q2bEl0dDSQHZ3q6+srFaPKlSvzwQcfsGHDBjp06MCPP/7I3r17pT7ub7zxBj/99FOuor579242b96MkZERo0ePplu3brz11ltStz1fX1+GDx/O2bNniYmJ4f79+1hZWTF27NgizU9+jh07RtOmTXnttdc4cOAA06ZNK/TvxpYtW5g6darUJbBq1ar89NNPuboG+vr68vTpU+lnuVzOpk2bpJ8vX77M66+/LjXQefPNNwkPD9d5AiE6OpoWLVoA2d34tm3bxqBBg1i9ejWQncAnk8moXLlyoeMuKQZV2HO69fUR7q7NPi2Tej8B81q2eh7Rvx4/foyDgwMymYzg4GBSUlJEURfKvbWX7+T5+luONrzhkF1U9t/+h5jnabnWqVHZlMHO1QG49OQZpx4m4NOyXqH7vHv3Ltu2bWPnzp2sW7eO3bt3ExISQmhoKOPGjQOyv2B/9NFH/Pjjj1hbWzN58mQ8PDzIzMyka9eudO3aFX9/f9zc3Ojfvz+hoaGsWbOGpUuX6uzr2bNnLF++HCcnJ2bPns2JEyfo3bs3v//+O23btuXPP/+kc+fOXLx4kQcPHrB161aUSiVDhw7F1dUVAGtrawIDA7lz5w7vvPMOrq6unDp1ivXr17Nq1SpWrFhBSEgI5ubmDBw4kFatWrFv3z7s7e355JNPSEhIYOTIkbl6sv9XaGiolJR2+/btXGlt2pS758+fU6lSpVwF778/JycnExwczL59+1AqlXz44Yd069Yt3/2rVCq2bdvG7t27izw/eaXrQXYHvf79+9OsWTOmTp1apMJ++/ZtmjRpUuBnAqTim5+c6XSQ3c5X2/teq1GjRvzxxx8MGTKE48ePExcXJy1btGgRBw4c4MMPP3ylzXwMq1d8DrHH/iL1fnafY/NattR2a6/nEWX7+OOPadu2rfRtuUGDBrRs2VLPoxKE8ql58+bIZDLs7e1p3LgxxsbG2NnZkZz8bzZ8QkIClSpVwtbWFmNjY9atWycVEe2R1pUrV6QI1jfffJNr167l2petrS3z5s3Dy8uLM2fOkJiYSM+ePTlx4gQAR44coU+fPoSHh3P58mW8vb0ZO3YsarVaKgba/dnZ2XHo0CE8PT1Zvnw5iYmJUmtVOzs7zM3Nad8++3faxYsXOXLkCN7e3kyfPp2MjIxc4Tbwb5xrnz592LdvHwEBAdKy/4bTaDQaKcymKElut2/fxtnZGVNTU6ysrFi7dm2B62s/Z3Hn579SU1M5efIkrq6uNGzYEIVCkeefjVbO9Lj8AnmK47+NWfNKqPvwww85ePAgI0aMQKPR6Lxn3rx5HDx4kI0bNxITE/PS4ykqgz1ih+yC/qrbyBamcePGODk5kZqaqu+hCMIrVZQj7IH1HQtdp5W9Na3si5aPkPN0cX7Xg42MjPL9Ja+9iVUmk0m/kLUJbhcvXmTFihVA9nXUOXPm8PXXX+Ps7Cxdu7eysqJatWpERUVx6dIlPv74Y27evImbmxsTJ07Md3/fffcdDg4OfPbZZ0RGRrJs2bJcRUNbeOVyOZMmTSo0R117jT0sLIwdO3ZQrVo1AJydnbly5YpOyMpff/1FgwYNsLS0RKVSERcXh52dnbT86tWrNG3aVBpPQXOopVL928lT+zmLOz//9euvv5KVlcXw4cOB7DCd/fv307RpU2xsbHj+/Ll0JKxNp9N+5oiICKpXry5t6/bt2zg6OmJubi69VtipeAcHB44ePSr9HBsbS6tWrXTGWL16ddatWwdkJ97Fxsby6NEj4uLieP3117G2tuaNN94gMjIy1+Wd0mJwR+zam+LTnzzX70D+T3JyMp9//rn0Dfrdd98lLCws16kvQRD0w8bGhqysLB4/foxGo2HixIm5IktzJrOdO3eO5s2b07p1aym1zMHBgeTkZKpXr05SUhJnzpyRCpmrqyvr1q2jVatWmJiY0KJFC8LCwlCr1WRkZBAYGJhrTE+fPqV27doA/Pbbb6hUKqpUqUJiYiLPnj0jPT2ds2fPAtnXy7XRzfHx8dKXjfz06NEDpVIpFaQRI0YQFBQkJbklJyezcuVKRo0aBYCXlxeffPIJmZmZQPaNZ35+fjpnBerXr8+dO3dISUkhIyOD0aNHS19E0tLSSEtLy5VCp/Ui86O1f/9+li1bxp49e9izZw8//vgjoaGhaDQaOnToIN0lr9Fo2LVrF127dpU+85o1a4iPjweyn5CYPn06Dx8+1Nn+6tWrddLpchZ17dxHRkaSlJRESkoK4eHhvPnmm7m2oZ1rbRJdQkICAQEBZGZmkpWVxdWrV6VI4VfBoI7YZUlpyCpnN8g3tbfErqv+T7+vXLmSlStXYm5ujo+PDzKZrMwGYwhCRbVw4UJ8fX0B6NevX543Uc2dO5cdO3Ygl8ulxLSc3nvvPTw9Palbty7jxo0jKCiIHj160KtXLxYvXkxwcDCQffPZW2+9hbu7OxqNRkp7y2nw4MF8+OGHhIaGMnz4cPbv38+ePXvw8fFh+PDh1KlTh+bNm2NsbEy/fv04ffo0Hh4eZGVl5Uqgy4u/vz9TpkyhQ4cOtGrVihkzZjBu3DjpcbfJkydLBWrcuHF89dVXDB06FGtraywtLVm7dq1O6Ii5uTm+vr6MGTMGjUbDyJEjkclkeHp68u677+Ls7EyzZs3yHEtR5+fYsWPcv39f+vnp06fcuHFDKtYAtWrVwsnJifDwcKZMmcKiRYsYPnw4WVlZtGvXDg8PDwDpM48dOxYzMzNMTEyYO3eu9PhfUZmamjJz5kzGjh2LTCZjypQpWFpa8tdff/Hrr7/i6+vLwIEDmT17NuvWreOtt96ie/fuAPTu3RtPT0/pcbdXebBnUOluJ6NSSa9VhUFGrXF67RAW1i4vtL2ImDDCow/Rq9mYF+o8p1KppFNNSUlJrF+/nilTpuR784ehKA+pY4agPMyzSHcrHaGhobRv354qVaowduxYpk6dSuvWrfU9rDyV9BynpKTwzTffFOmLS3lUkuluBnUqXlNZ/4XzwoULdOjQgbCwMCD7GtLMmTMNvqgLgqB/aWlpjBw5Eg8PD2rXrl1mi3ppePLkCf3799f3MMoFgzoVr7E0RWOk357qJiYm3L9/n8jISHr06KHXsQiCUL4MHTqUoUOH6nsYelG3bl19D6HcMKjCjqkCjVrzytvFnz59mrp16+Lo6EjLli25ePGizt2WgiAIglBWGNSpeH04ffo0AwYMYNasWdJroqgLgiAIZZVhHbFD9vNur/CIvV27dnh5eeHp6fnqdioIgiAIL8jwCjtgaTsMU4u2Oq9pNBrO3N5DQso/hb4/JSMx32VpaWksWbIER0dHJk+ejJGREatWrXrpMQuCIAjCq2B4hV0to2aj7bleVmVlcP3RaQBkRYiLqWRigZWZXa7XU1NT2b59O3Z2dkyYMKFMpBsJgiAUVVBQEPv27cPBwQGNRkN6ejoTJ06kV69eQHb73GXLlpGWloZKpcLFxYXJkydjbJx9Y3JJxZ2Wln379uHn58fx48elPu5+fn706dNH54bmnLGuERERfPbZZ2RkZEifecqUKbnawxbm+vXrUqvexo0b89FHH+ksf/z4MXPmzEGpVKJWq/H396d58+ao1WpWrlzJzp07OX369MtNQBEYVNUy0ihBU/CQnWxfw6XpyGJtNz09nUePHlGvXj2qVq3Kzp07cXZ2FkVdEASDNGLECLy8vABITExkyJAhdOnShczMTGbOnMmqVato0qQJGo2GxYsXExQUxPvvv19icaelaf/+/Tg5OUm99guTnJzMrFmzCAoKolGjRqhUKt5//3127tzJu+++W6x9L168mDlz5tCiRQumT5/OH3/8oROG8+2339KrVy88PDwIDw9n5cqVbNy4ka+//hpHR8dcvedLi0FVLgvukKwuPIu3ONLT03FxcSEzM5OjR49iZmYmBRgIglB2FRbb2r59e5YsWUJERAQZGRl4enryzjvv8ODBA/z8/MjKyqJGjRosXbqUuXPnSpGqK1asYMGCBcTExKBUKvH19aVz5846+/7nn3+kG2ozMzNZunQpv//+O8nJyVKDFW9vb+bNm0d0dDSbNm3CxMSE5s2b4+fnR0hICMeOHSM2NpaVK1eyadOmXOO8fv06fn5+WFpa0rZtWx4+fMinn37K1q1b2bdvH0ZGRri6ujJmzJgC56lKlSrY29vz5MkTTpw4gYuLi5R8JpPJ+OCDD+jTpw/Tp08vctzptWvXWLBgASYmJrRu3ZoPP/wQb29v5s+fT6NGjdiyZQtPnz6lXbt2bNq0idTUVHr06EFqamqR5ic/iYmJREREsGTJEjZu3Fikwr5v3z5cXFykhE25XM7SpUsxMzPTWW/t2rWcPHlS57WFCxdK3eqUSiUPHjyQ6oOLiwunTp3SKew2NjYkJmZf6k1KSsLGxgbIbttbuXLlQtPkSopBFfYsKpFlVLI38puamtK9e3dUKtUr+zYlCOVRWYttbd26NTVr1sTf35/09HRcXV155513pD7pLi4uLFu2jCtXrgD/Rqru3r0bhULBli1bePz4Md7e3hw+fFhnv7GxsUyZMoX27duza9cutm3bxogRI/D19WXq1KkkJiaSkJBArVq18PPzY/v27SgUCqZPn86FCxcAePToET/++CNKpTLPcQYHBzNlyhR69erF9OnTMTMzIyYmhtDQUH744QcAPD096du3LzVq1Mh3jm7fvk18fDwODg7cvn07V9qkubk5dnZ2xMbGFjnuNDAwkLlz59K6dWtmz57NgwcP8t3/jRs3OHToEHFxcUWen/w6Mx48eJAePXrQpUsX5s2bJ0VkF+T27du5Dtbyykb38fHBx8cn3+08ffpUZy60X5ZyGjVqFG5ubuzevZvk5GTpz+lVZrGDgRX2TCyLcPW8cJcuXeLgwYP4+/sD2Zm5xb3WIgiC/hUU21qpUiWePXuGh4cHcrlcSvG6du0ac+fOBWD27NkA/PDDDzoRrm+99RaQne5lbGxMYmIiVapUkfZrb2/PokWLCAoKIikpiWbNmknFNTY2VooavXXrFg8fPmTs2LEAPH/+XAoief3115HJZPmOMyoqSipwPXr04PTp00RGRhIdHc2IESOA7DasDx48yFXYv//+ew4dOkRycjJKpZLly5dLrUrzimnNGeNalLjT6Oho6Qh42bJlBa7buHFjFApFseYnv8K+f/9+pkyZgrGxMX379uXgwYNSmE1etL/XixJNW1x5HQhu2LCBfv364ePjQ1hYGEuXLmXNmjUlvu/CGFRhV8qqInvJA3aNRoOfnx9nz57l7bffplmzZqKoC0IJKGuxrWfPnuX06dNs3rwZuVwutWc1NjbO85eyNv8BdH9pq9VqlEol3t7eAIwdO5ZDhw7RuXNnPD09CQ0NldK9XF1dOXr0KCdOnGDSpElA9pePjRs36uwrJCRE2l9+48w5hpwRrt27d5diY/OjvcYeGxvLyJEjady4MfBvhOvgwYOldVNSUnj27Bn29vZFjjst7HemNikOdHufF3V+8vLo0SMiIiL49NNPkclkpKenY2lpyahRo7CxscmV2KdSqTA3N8fZ2ZnIyEiGDBkiLUtISCAtLY2aNWtKrxV2Kt7W1lY6zQ7ZN8ppo3G1wsPDef/99wHo1KlTrpvrXhWDa1DzoqfLtX/oMpmMVatW8fPPP+ebRiQIguF7+vQpjo6OyOVyjhw5QlZWFkqlkubNm0t3Jq9atSrXL/OcEa6PHj3CyMiIatWqSdGe3bt3l2JXNRoNR44ckSJce/fuzR9//MG9e/do2rQp9erVIyoqSooPXb16NY8fPy7SOGvXri1dJjh27BgAzZo148yZM6SlpaHRaFi0aBHp6en5zkG1atUYMmSIdNQ4aNAgjh49SmRkpLTOypUreeedd4Cix51qiyXAnDlziIqKonLlytKp6fDw8DzH8yLzo7V//36GDx/O3r172bNnD6GhoTx79ox79+7RoUMH9u/fL32h2L9/v3TUr/3MERERQPa18oCAgFx/7j4+PjoRrps3b9ZJg5PL5dSvX5/z588DcPjwYbp06aKzjTp16nD58mUAIiIiqFOnTp6fpbQZ1BE7vFhhX716NatWreLo0aM4OTnRuHFj6RusIAjlU8eOHVm/fj1eXl64urrSvXt3AgIC8PX1xd/fn23btlG9enWmTp3K3r17pfcNGDCAs2fP4u3tjUqlyvPo2N3dnUWLFlGjRg3pprETJ07QuXNn7t27J0WNmpmZMWfOHMaPH49CoaBp06a5jvLyG6ePjw/z5s3ju+++o0GDBiQnJ1OjRg1GjBjB8OHDMTY2xtXVtdAAqtGjRzNo0CD+97//0bBhQ77++msCAgJISUkhMzOT3r17S0fPRY07nTt3LgsWLMDIyIhWrVrh7OyMu7s7H3/8MXXq1JGy5v+rfv36RZqfJ0+eEBQUpDP3v/zyi85pf5lMxpAhQ/jll1/w8fEhKiqK4cOHo1AosLOzY8GCBQBYWFiwfv16Fi5cSHp6OsbGxgwaNEj6MlMcc+bMYcGCBajValq2bEnHjh2B7C8Fa9euZeLEicydO5fQ0FBpniD7noQbN26QnJyMt7c3PXv2ZPTo0cXef1EZVGzrCXVlnmeqmdchd66tMjOdbacD8nzcbdu2bSxZsoRvvvlGyiAWcisPcaKGoDzMs4htLX2XLl3C1NSUJk2asG7dOgAmTpyo51H9q7TneOnSpXz44Yeltv2ypiRjWw3uiL0oVCoV33//PSNGjEAul+Pp6cnbb7/9yu9MFARBeFFyuZy5c+diamqKqakpn3/+ub6H9MoolUo6deqk72EYLIMq7MaaVKDw3PPPP/+cZcuWkZqayrRp05DJZKKoC4JgUJo1a8ZPP/2k72HohUKhyNU7QCg6gyrs5sSQSMM8l+V8RMPHx4fk5GRGjixeBzpBEARB0AeNRlNiT2gZ1F3xmnyGe/36dfr1HcDNy9lNEqytrVm0aFGejRUEQXh5RkZGOo80CYLwcrKysqTHGl+WQR2xa/JpT5OamsqVyCvYNZDRs0eeqwiCUIJMTExIS0sjNTUVY2PjMtcLQqVSSTcjCaVDzHHJ0Gg0ZGVlkZWVVWL5JAZ1xJ7T9evXiY2NBeCNN97g2ImjDBjRTs+jEoSKw9LSEoVCUeaKOmR3bRNKl5jjkiGTyVAoFFhaWpbYNkv1iP2TTz7h8uXLyGQyKRFH6+TJk6xYsQJjY2O6du3KlClTirzdS5cu0bdvX3r37s33338PZDdMOPOkkDcKglCiynICYll9FK88EXNcNpXaEfvZs2eJjo5m+/btLFq0iMDAQJ3l2j7LP/zwA8ePH+fWrVtF3naLFi0YMGAA7733XkkPWxAEQRAMWqkV9lOnTuHq6gpAgwYNSEpKIjk5GYCYmBisra2pXr06RkZGdOvWjVOnThW6zfAjF5DJQJmVRvBXq+nu0oV0VQrpqhQyMlNL66MIgiAIgsEotfNocXFxOr3Yq1atypMnT6R+wra2ttIyOzs7YmJi8t2Wtjne7dPhNOw2kJ/O5t2oQS4zx0itICMjo4Q+RcUj5u7VEPNc+sQclz4xx6VLe3NicRvEllph/+9Acj6jl9cgC7oBRxuwsHjBfEADDMh/xxlIwQlC8Ym5ezXEPJc+McelT8zxq6FSqQrNBMip1Aq7g4MDcXFx0s+xsbHY2dnluezx48fY29vnuy0LCwsaNWqEXC4vk3fgCoIgCEJJ02g0qFSqYvfkL7XC3qlTJ4KCgvDw8ODatWtUq1ZNautaq1YtkpOTuX//Po6OjoSFhbF8+fJ8t2VkZFSijwIIgiAIgiEozpG6Vqmmuy1fvpzz588jk8lYuHAh165dw9LSkl69enHu3DmpmPfu3ZuxY8eW1jAEQRAEocIwiNhWQRAEQRCKxmA7zwmCIAiCkJso7IIgCIJQjpTJwv7JJ5/g7u6Oh4cHEREROstOnjyJm5sb7u7uBAcH62mEhq+gOT59+jTvvvsuHh4e+Pv760TiCkVX0Bxrff7553h7e7/ikZUfBc3xo0eP8PT0xM3NjQULFuhphOVDQfO8detW3N3d8fT0ZPHixXoaoeG7ceMGrq6ubNmyJdeyYtc9TRlz5swZzYQJEzQajUZz8+ZNjZubm87yfv36aR4+fKjJysrSuLu7a27evKmPYRq0wua4V69emkePHmk0Go1m2rRpmqNHj77yMRq6wuZY+7q7u7vGy8vrVQ+vXChsjn19fTWHDx/WaDQaTUBAgObBgwevfIzlQUHz/Pz5c02PHj00KpVKo9FoNKNHj9ZcvHhRL+M0ZCkpKRovLy/NvHnzNJs3b861vLh1r8wdsZdGK1pBV0FzDBASEoKjoyMAtra2PH36VC/jNGSFzTHAp59+yowZM/QxvHKhoDlWq9VcuHCBnj17ArBw4UJq1Kiht7EasoLmWS6XI5fLSU1NJTMzk7S0NKytrfU5XIOkUChYv3491apVy7XsRepemSvscXFx2NjYSD9rW9ECebai1S4Tiq6gOQakfgOxsbGcPHmSbt26vfIxGrrC5jgkJIR27dpRs2ZNfQyvXChojhMSEqhcuTKrV6/Gy8uLzz//vNhtOYVsBc1zpUqVmDJlCq6urvTs2ZNWrVpRr149fQ3VYJmYmOT7vPqL1L0yV9j/+49P8xKtaIW8FTTHWvHx8UyaNIkFCxbo/KMWiqagOU5MTCQkJITRo0frY2jlRmG/Kx4/fsywYcP47rvvuHbtGn/88Yc+hmnwCprn5ORk1q1bR2hoKL/99huXLl3i+vXr+hhmufUida/MFfaSbEUr5K2gOYbsf6zjx49n+vTpdO4x5DpeAAAHqklEQVTcWR9DNHgFzfHp06dJSEhg+PDhTJ06latXr/LJJ5/oa6gGq6A5trGxoXr16tSuXRtjY2M6dOjAzZs39TVUg1bQPEdFReHk5IStrS0KhYI333xT9I8vYS9S98pcYe/UqROHDh0CKLAVbWZmJmFhYXTq1EmfwzVIBc0xZF/7HTlypDgF/xIKmuO+ffty4MABduzYwZo1a2jWrBlz5szR53ANUkFzbGJigpOTE3fv3gXg6tWr4hTxCyponmvWrElUVBTp6eloNBquXLlC3bp19Tja8udF6l6Z7DwnWtGWvvzmuHPnzrRt25bWrVtL6w4cOBB3d3c9jtYwFfT3WOv+/fv4+/uzefNmPY7UcBU0x9HR0SxcuJCMjAwaNmxIQEAARkZl7ljGIBQ0zz/++CMhISEYGxvTunVrZs+ere/hGpwrV66wdOlSHjx4gImJCQ4ODvTs2ZNatWq9UN0rk4VdEARBEIQXI76+CoIgCEI5Igq7IAiCIJQjorALgiAIQjkiCrsgCIIglCOisAuCIAhCOWKi7wEIQkVw//59+vbtq/MYIcCcOXN47bXX8nxPUFAQmZmZL9VP/syZM0yePJmmTZsCkJGRQdOmTZk7dy5yubxY2zp27BhXr17Fx8eH8PBw7O3tcXJyYvHixQwePJjmzZu/8DiDgoIICQmhVq1aAKhUKqpXr87HH3+MpaVlvu97/Pgxt2/fpkOHDi+8b0Eob0RhF4RXxNbWVi/Pqzdq1Ejar0ajYcaMGezYsYPhw4cXaztdu3ala9euQHav+/79++Pk5MTcuXNLZJxvv/22zpeYzz77jK+++opZs2bl+54zZ84QFRUlCrsg5CAKuyDoWVRUFAsXLsTY2Jjk5GTef/99unTpIi3PzMxk3rx53LlzB5lMxmuvvcbChQtRKpV8/PHHREdHo1arcXFxYcyYMQXuSyaT0aZNG6KiogA4evQowcHBmJqaYmZmRmBgIA4ODixfvpzTp0+jUCioVq0ay5YtY//+/Zw8eZI+ffoQGhpKREQE/v7+fPnll/j4+PD5558zb9486azEqFGjGD16NA0bNuSjjz4iIyMDlUrFlClT6NixY6Hz0rp1a3bs2AHA+fPnWb58OQqFgvT0dBYuXIiVlRVffPEFGo2GKlWqMHz48GLPhyCUR6KwC4KexcXFMX36dNq2bcvFixcJDAzUKew3btzg8uXLHDx4EIAdO3bw/Plztm/fTrVq1Vi0aBFZWVm8++67dOzYkSZNmuS7r4yMDMLCwnBzcyMtLY158+axa9cuHB0d2bJlC1988QV+fn5s3bqV8+fPY2xszIEDB3R6Vffq1Yvvv/8eHx8fOnTowJdffgnAoEGDCA0NpXXr1sTHxxMVFUWnTp2YPHkyY8aMoX379jx58gR3d3cOHz6MiUn+v34yMzPZv38/rVq1ArKDcwICAmjSpAn79+9n3bp1rF69mqFDh5KZmcno0aPZsGFDsedDEMojUdgF4RVJSEjA29tb57VVq1Zhb2/PsmXLWLlyJSqVisTERJ11nJ2dsbGxYfz48fTo0YN+/fphaWnJmTNn+Oeffzh37hwASqWSe/fu5SpkN27c0Nlvjx496N+/P3/99RdVq1bF0dERgHbt2vHjjz9ibW1Nly5d8PLyolevXvTv319apyADBgzAw8MDf39/QkND6devHyYmJpw5c4aUlBSCg4OB7D7u8fHxODg46Lx/7969hIeHo9FouHbtGiNGjGDChAlAdlTlZ599RkZGBklJSXlmfhd1PgShvBOFXRBekfyusc+cOZMBAwbg5ubGjRs3mDRpks7ySpUqsW3bNq5evSodbf/www8oFAqmTJlC3759C9xvzmvsBckZx7l69WqioqL4448/8PLyIigoqND329vbU7t2bSIiIjh48CB+fn4AKBQKgoKCdDKl85LzGvukSZOoWbOmdFQ/e/ZsPvroIzp06EBYWBibNm3K9f6izocglHficTdB0LO4uDhq164NwIEDB1AqlTrLIyMj+fnnn2nWrBlTp06lWbNm3L17lzZt2hAaGgqAWq1myZIluY72C1KvXj3i4+N5+PAhAKdOnaJly5bExMTw7bff4uzszJgxY+jVq1eujG2ZTEZ6enqubQ4aNIhdu3bx7Nkz6S75Nm3aSJcREhISihRRu3DhQtasWcM///yjM0dqtZrQ0FBpjmQyGRkZGdJ+XmY+BKG8EEfsgqBnY8aMYf78+dSqVYtRo0Zx+PBhPv30UywsLACoXbs2wcHBbN++HYVCQe3atXnjjTdo2bIlN2/exN3dnaysLLp3706VKlWKvF9TU1MWL17MjBkzUCgUmJubs3jxYqysrLh27Rpubm5YWFhgbW3NlClTOHz4sPTeTp068dFHH5GZmamzzd69exMYGMjEiROl1+bOncuCBQv45ZdfUCqV+Pj4FDq26tWrM27cOObPn8/69esZP348EyZMoEaNGowdO5bZs2fz7bff8uabbzJjxgxMTU3x8fF5qfkQhPJCpLsJgiAIQjkiTsULgiAIQjkiCrsgCIIglCOisAuCIAhCOSIKuyAIgiCUI6KwC4IgCEI5Igq7IAiCIJQjorALgiAIQjkiCrsgCIIglCP/HzIOECBMsrUDAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# **This is an interesting way of seeing how our model create boundry and how it fits the space of features, below you can see our model is overfit**","metadata":{}},{"cell_type":"code","source":"plot_model(rf, plot = 'boundary')","metadata":{},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Do you see the difference in boundries of models (normal and tuned)**","metadata":{}},{"cell_type":"code","source":"plot_model(tuned_rf, plot = 'boundary')","metadata":{},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3. Oversampling to balance the classes","metadata":{}},{"cell_type":"code","source":"from imblearn.over_sampling import *\nadasyn1 = ADASYN(sampling_strategy='minority')","metadata":{},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":"# **Here i have worked and tweaked some of the functions of the pipeline and try to balance the imbalance classes**","metadata":{}},{"cell_type":"code","source":"Session_2 = setup(df1, target = 'quality', session_id=177, log_experiment=False, \n                  experiment_name='wine_q2', normalize=True, normalize_method='zscore', \n                  transformation=True, transformation_method = 'quantile', fix_imbalance=True,\n                  fix_imbalance_method= adasyn1)","metadata":{},"execution_count":24,"outputs":[{"output_type":"stream","text":"Setup Succesfully Completed!\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97a892a890>","text/html":"<style  type=\"text/css\" >\n    #T_38688b18_d612_11ea_8583_0242ac130202row15_col1 {\n            background-color:  lightgreen;\n        }    #T_38688b18_d612_11ea_8583_0242ac130202row17_col1 {\n            background-color:  lightgreen;\n        }    #T_38688b18_d612_11ea_8583_0242ac130202row42_col1 {\n            background-color:  lightgreen;\n        }</style><table id=\"T_38688b18_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >session_id</td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >177</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >Target Type</td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >Multiclass</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >Label Encoded</td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >Original Data</td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >(1599, 12)</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >11</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >10</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >(1599, 12)</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >11</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >(1119, 11)</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row12\" class=\"row_heading level0 row12\" >12</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row12_col1\" class=\"data row12 col1\" >(480, 11)</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row13\" class=\"row_heading level0 row13\" >13</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row13_col1\" class=\"data row13 col1\" >mean</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row14\" class=\"row_heading level0 row14\" >14</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row14_col1\" class=\"data row14 col1\" >constant</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row15\" class=\"row_heading level0 row15\" >15</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row15_col0\" class=\"data row15 col0\" >Normalize </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row15_col1\" class=\"data row15 col1\" >True</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row16\" class=\"row_heading level0 row16\" >16</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row16_col1\" class=\"data row16 col1\" >zscore</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row17\" class=\"row_heading level0 row17\" >17</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row17_col0\" class=\"data row17 col0\" >Transformation </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row17_col1\" class=\"data row17 col1\" >True</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row18\" class=\"row_heading level0 row18\" >18</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row18_col1\" class=\"data row18 col1\" >quantile</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row19\" class=\"row_heading level0 row19\" >19</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row19_col0\" class=\"data row19 col0\" >PCA </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row19_col1\" class=\"data row19 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row20\" class=\"row_heading level0 row20\" >20</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row20_col1\" class=\"data row20 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row21\" class=\"row_heading level0 row21\" >21</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row21_col1\" class=\"data row21 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row22\" class=\"row_heading level0 row22\" >22</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row22_col1\" class=\"data row22 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row23\" class=\"row_heading level0 row23\" >23</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row23_col1\" class=\"data row23 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row24\" class=\"row_heading level0 row24\" >24</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row24_col1\" class=\"data row24 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row25\" class=\"row_heading level0 row25\" >25</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row25_col1\" class=\"data row25 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row26\" class=\"row_heading level0 row26\" >26</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row26_col1\" class=\"data row26 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row27\" class=\"row_heading level0 row27\" >27</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row27_col1\" class=\"data row27 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row28\" class=\"row_heading level0 row28\" >28</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row28_col1\" class=\"data row28 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row29\" class=\"row_heading level0 row29\" >29</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row29_col1\" class=\"data row29 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row30\" class=\"row_heading level0 row30\" >30</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row30_col0\" class=\"data row30 col0\" >Clustering </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row30_col1\" class=\"data row30 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row31\" class=\"row_heading level0 row31\" >31</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row31_col1\" class=\"data row31 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row32\" class=\"row_heading level0 row32\" >32</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row32_col1\" class=\"data row32 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row33\" class=\"row_heading level0 row33\" >33</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row33_col1\" class=\"data row33 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row34\" class=\"row_heading level0 row34\" >34</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row34_col1\" class=\"data row34 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row35\" class=\"row_heading level0 row35\" >35</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row35_col1\" class=\"data row35 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row36\" class=\"row_heading level0 row36\" >36</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row36_col0\" class=\"data row36 col0\" >Group Features </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row36_col1\" class=\"data row36 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row37\" class=\"row_heading level0 row37\" >37</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row37_col1\" class=\"data row37 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row38\" class=\"row_heading level0 row38\" >38</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row38_col1\" class=\"data row38 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row39\" class=\"row_heading level0 row39\" >39</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row39_col1\" class=\"data row39 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row40\" class=\"row_heading level0 row40\" >40</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row40_col1\" class=\"data row40 col1\" >False</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row41\" class=\"row_heading level0 row41\" >41</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row41_col1\" class=\"data row41 col1\" >None</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row42\" class=\"row_heading level0 row42\" >42</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row42_col0\" class=\"data row42 col0\" >Fix Imbalance</td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row42_col1\" class=\"data row42 col1\" >True</td>\n            </tr>\n            <tr>\n                        <th id=\"T_38688b18_d612_11ea_8583_0242ac130202level0_row43\" class=\"row_heading level0 row43\" >43</th>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row43_col0\" class=\"data row43 col0\" >Fix Imbalance Method</td>\n                        <td id=\"T_38688b18_d612_11ea_8583_0242ac130202row43_col1\" class=\"data row43 col1\" >ADASYN</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"**You can several of the func are now shahed green because i will be envolving them in pipeline**","metadata":{}},{"cell_type":"code","source":"best_model1 = compare_models(blacklist=['catboost'])","metadata":{},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97a898e490>","text/html":"<style  type=\"text/css\" >\n    #T_89ce10ae_d612_11ea_8583_0242ac130202 th {\n          text-align: left;\n    }    #T_89ce10ae_d612_11ea_8583_0242ac130202row0_col0 {\n   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         : ;\n            text-align:  left;\n        }    #T_89ce10ae_d612_11ea_8583_0242ac130202row13_col5 {\n            : ;\n            text-align:  left;\n        }    #T_89ce10ae_d612_11ea_8583_0242ac130202row13_col6 {\n            : ;\n            text-align:  left;\n        }    #T_89ce10ae_d612_11ea_8583_0242ac130202row13_col7 {\n            : ;\n            text-align:  left;\n        }    #T_89ce10ae_d612_11ea_8583_0242ac130202row13_col8 {\n            background-color:  lightgrey;\n            text-align:  left;\n        }</style><table id=\"T_89ce10ae_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>        <th class=\"col_heading level0 col7\" >MCC</th>        <th class=\"col_heading level0 col8\" >TT (Sec)</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >Extreme Gradient Boosting</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.6515</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.3496</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6383</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.6396</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4443</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row0_col7\" class=\"data row0 col7\" >0.4481</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row0_col8\" class=\"data row0 col8\" >1.0652</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >Extra Trees Classifier</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.6497</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.3097</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6259</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.6309</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4301</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row1_col7\" class=\"data row1 col7\" >0.4349</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row1_col8\" class=\"data row1 col8\" >1.4918</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >Light Gradient Boosting Machine</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.6461</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.3306</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6263</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.6305</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4296</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row2_col7\" class=\"data row2 col7\" >0.4336</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row2_col8\" class=\"data row2 col8\" >1.2280</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >Gradient Boosting Classifier</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.6354</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.3097</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6278</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.6250</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4168</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row3_col7\" class=\"data row3 col7\" >0.4202</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row3_col8\" class=\"data row3 col8\" >2.6186</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >Random Forest Classifier</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.6220</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.3229</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.6041</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.6080</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3951</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row4_col7\" class=\"data row4 col7\" >0.3981</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row4_col8\" class=\"data row4 col8\" >0.2860</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >Ada Boost Classifier</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.5818</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.3097</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.5792</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.5724</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.3321</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row5_col7\" class=\"data row5 col7\" >0.3356</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row5_col8\" class=\"data row5 col8\" >1.1472</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >Quadratic Discriminant Analysis</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.5800</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.2989</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.5601</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.5669</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.3260</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row6_col7\" class=\"data row6 col7\" >0.3277</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row6_col8\" class=\"data row6 col8\" >0.0245</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >Logistic Regression</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.5505</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.2851</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.5619</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.5365</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.2852</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row7_col7\" class=\"data row7 col7\" >0.2917</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row7_col8\" class=\"data row7 col8\" >0.0973</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >K Neighbors Classifier</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.5478</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.2949</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.5546</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.5470</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.3001</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row8_col7\" class=\"data row8 col7\" >0.3029</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row8_col8\" class=\"data row8 col8\" >0.0293</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >Linear Discriminant Analysis</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.5327</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.2970</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.5685</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5424</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.2861</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row9_col7\" class=\"data row9 col7\" >0.2910</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row9_col8\" class=\"data row9 col8\" >0.0377</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >10</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >Naive Bayes</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.5220</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.3211</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.5640</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.5374</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.2839</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row10_col7\" class=\"data row10 col7\" >0.2882</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row10_col8\" class=\"data row10 col8\" >0.0245</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >11</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >Ridge Classifier</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.5005</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.2507</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.4929</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.4921</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.2346</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row11_col7\" class=\"data row11 col7\" >0.2441</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row11_col8\" class=\"data row11 col8\" >0.0336</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row12\" class=\"row_heading level0 row12\" >12</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row12_col0\" class=\"data row12 col0\" >Decision Tree Classifier</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row12_col1\" class=\"data row12 col1\" >0.4907</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row12_col2\" class=\"data row12 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row12_col3\" class=\"data row12 col3\" >0.2879</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row12_col4\" class=\"data row12 col4\" >0.6113</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row12_col5\" class=\"data row12 col5\" >0.5380</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row12_col6\" class=\"data row12 col6\" >0.2845</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row12_col7\" class=\"data row12 col7\" >0.2959</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row12_col8\" class=\"data row12 col8\" >0.0830</td>\n            </tr>\n            <tr>\n                        <th id=\"T_89ce10ae_d612_11ea_8583_0242ac130202level0_row13\" class=\"row_heading level0 row13\" >13</th>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row13_col0\" class=\"data row13 col0\" >SVM - Linear Kernel</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row13_col1\" class=\"data row13 col1\" >0.4862</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row13_col2\" class=\"data row13 col2\" >0.0000</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row13_col3\" class=\"data row13 col3\" >0.2934</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row13_col4\" class=\"data row13 col4\" >0.5113</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row13_col5\" class=\"data row13 col5\" >0.4851</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row13_col6\" class=\"data row13 col6\" >0.2141</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row13_col7\" class=\"data row13 col7\" >0.2186</td>\n                        <td id=\"T_89ce10ae_d612_11ea_8583_0242ac130202row13_col8\" class=\"data row13 col8\" >0.0592</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"xgboost_1 = create_model('xgboost')","metadata":{},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97c43ef710>","text/html":"<style  type=\"text/css\" >\n    #T_8d6e2654_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_8d6e2654_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_8d6e2654_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_8d6e2654_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_8d6e2654_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_8d6e2654_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_8d6e2654_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_8d6e2654_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.5804</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.2934</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.5779</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.5770</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.3509</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.3525</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.7143</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.5190</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6892</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6997</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.5423</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.5442</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6161</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3024</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.5925</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6033</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.3821</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.3833</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6429</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.3357</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6235</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6315</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.4335</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4356</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.5625</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2513</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5258</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5382</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.2878</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.2917</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6875</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3355</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6545</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6668</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4932</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4970</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6161</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2915</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6065</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6075</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.3928</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.3967</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6875</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.4202</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6574</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6708</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.4918</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.4940</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6786</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.3381</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6528</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6632</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.4799</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4830</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6847</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.3496</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6662</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6750</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5003</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5008</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6470</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3437</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6246</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6333</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4355</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4379</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8d6e2654_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0487</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0723</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0469</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0483</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0760</td>\n                        <td id=\"T_8d6e2654_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0757</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"et_1 = create_model('et')","metadata":{},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97c4383e90>","text/html":"<style  type=\"text/css\" >\n    #T_9bf6495e_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_9bf6495e_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_9bf6495e_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_9bf6495e_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_9bf6495e_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_9bf6495e_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_9bf6495e_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_9bf6495e_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6250</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3115</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.5935</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6083</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.4003</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4019</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6786</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3170</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6496</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6573</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4789</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4857</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6518</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.2983</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6211</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6324</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4321</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4349</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6161</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.2996</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6066</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6033</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.3786</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.3829</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.6071</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2609</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5535</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5703</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3508</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3587</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6429</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3179</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6370</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6317</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4228</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4267</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6696</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.3224</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6561</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6534</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.4689</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.4778</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6518</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3188</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6244</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6351</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.4364</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.4398</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6786</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.3219</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6724</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6650</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.4733</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4777</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6757</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.3377</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6467</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6588</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.4715</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.4739</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6497</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3106</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6261</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6315</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4313</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4360</td>\n            </tr>\n            <tr>\n                        <th id=\"T_9bf6495e_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0252</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0198</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0330</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0285</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0417</td>\n                        <td id=\"T_9bf6495e_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0418</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"lgbm_1 = create_model('lightgbm')","metadata":{},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e0db3690>","text/html":"<style  type=\"text/css\" >\n    #T_a2a5520e_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_a2a5520e_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_a2a5520e_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_a2a5520e_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_a2a5520e_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_a2a5520e_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_a2a5520e_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_a2a5520e_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6339</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3237</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6221</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6244</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.4242</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4280</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.7054</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3443</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6701</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6832</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.5211</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.5259</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6696</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3146</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6345</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6501</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4627</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4647</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6250</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.3034</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6057</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6113</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.3980</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4014</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.5982</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2739</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5554</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5693</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3382</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3437</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6518</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3048</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6398</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6313</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4293</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4352</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6250</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2872</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6063</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6092</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.3991</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.4053</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6696</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.4215</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6455</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6558</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.4697</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.4719</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6607</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.4063</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6437</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6458</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.4551</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4618</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6847</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.4016</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6828</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6831</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5022</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5024</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6524</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3381</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6306</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6363</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4400</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4440</td>\n            </tr>\n            <tr>\n                        <th id=\"T_a2a5520e_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0305</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0505</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0342</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0333</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0512</td>\n                        <td id=\"T_a2a5520e_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0504</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"gbc_1 = create_model('gbc')","metadata":{},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e0d37590>","text/html":"<style  type=\"text/css\" >\n    #T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6250</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3197</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6219</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6231</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.4170</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4178</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6696</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3133</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6676</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6609</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4740</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4789</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.5893</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.2735</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.5970</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.5904</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.3581</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.3607</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.5804</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.2851</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.5677</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.5688</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.3234</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.3264</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.5982</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2658</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5605</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5710</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3431</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3487</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6250</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3019</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6182</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6158</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4023</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4059</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.5804</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2605</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.5597</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.5625</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.3222</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.3266</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6161</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3562</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6042</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6076</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.3857</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.3875</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6429</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.2984</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6314</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6268</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.4166</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4216</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6847</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.3414</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6722</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6781</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.4995</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5000</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6211</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3016</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6100</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6105</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.3942</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.3974</td>\n            </tr>\n            <tr>\n                        <th id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0343</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0301</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0385</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0368</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0572</td>\n                        <td id=\"T_aacbb2f2_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0567</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"tuned_xgboost1 = tune_model(xgboost_1)","metadata":{},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97a891cf10>","text/html":"<style  type=\"text/css\" >\n    #T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.5804</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3015</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.5606</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.5694</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.3431</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.3440</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6696</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.4925</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6397</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6521</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4708</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4727</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.5804</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.2698</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.5635</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.5712</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.3316</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.3325</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6161</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.3078</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6040</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6069</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.3864</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.3885</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.5804</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2670</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5545</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5583</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3142</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3188</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6607</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3627</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6600</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6536</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4566</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4593</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.5893</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2724</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.5841</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.5828</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.3521</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.3551</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6786</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.4150</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6569</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6605</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.4808</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.4890</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6161</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.3126</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6054</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6058</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.3901</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.3946</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6486</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.3187</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6427</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6453</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.4455</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.4459</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6220</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3320</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6072</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6106</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.3971</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4000</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0374</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0688</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0387</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0375</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0588</td>\n                        <td id=\"T_d68f2fb8_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0594</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"tuned_lgbm1 = tune_model(lgbm_1)","metadata":{},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97a88d93d0>","text/html":"<style  type=\"text/css\" >\n    #T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.5893</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3052</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.5975</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.5910</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.3689</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.3704</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6429</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.4651</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6275</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6302</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4332</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4351</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6429</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3228</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6435</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6420</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4446</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4456</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6339</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.3319</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6207</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6243</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.4146</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4173</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.5804</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2748</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5653</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5643</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3286</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3353</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6607</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.4006</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6605</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6553</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4636</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4674</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.5446</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2547</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.5453</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.5398</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.2855</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.2891</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.7232</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.4896</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.7050</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.7124</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.5549</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.5564</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6518</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.3361</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6695</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6580</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.4533</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4550</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6847</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.4010</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6844</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6768</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5019</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5050</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6354</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3582</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6319</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6294</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4249</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4277</td>\n            </tr>\n            <tr>\n                        <th id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0496</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0743</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0487</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0495</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0759</td>\n                        <td id=\"T_d8d3ac40_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0751</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"tuned_et1 = tune_model(et_1)","metadata":{},"execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e330c390>","text/html":"<style  type=\"text/css\" >\n    #T_f047eb3e_d612_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_f047eb3e_d612_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_f047eb3e_d612_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_f047eb3e_d612_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_f047eb3e_d612_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_f047eb3e_d612_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_f047eb3e_d612_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_f047eb3e_d612_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6518</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3306</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6211</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6353</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.4455</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4473</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6786</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3086</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6492</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6568</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4794</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4854</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6339</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.2911</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6093</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6193</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4091</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4108</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.5893</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.2805</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.5821</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.5754</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.3326</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.3374</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.6071</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2690</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5659</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5734</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3505</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3592</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6607</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3251</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6470</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6482</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4537</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4574</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6339</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2826</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6038</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6084</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.4029</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.4121</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6250</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3079</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.5987</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6082</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.3928</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.3966</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6339</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.2949</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6282</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6167</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.3965</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4017</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6937</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.3973</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6901</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6847</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5044</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5069</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6408</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3088</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6195</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6226</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4167</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4215</td>\n            </tr>\n            <tr>\n                        <th id=\"T_f047eb3e_d612_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0299</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0349</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0343</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0331</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0515</td>\n                        <td id=\"T_f047eb3e_d612_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0505</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"code","source":"tuned_gbc1 = tune_model(gbc_1)","metadata":{},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e32fa390>","text/html":"<style  type=\"text/css\" >\n    #T_182d37bc_d613_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_182d37bc_d613_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_182d37bc_d613_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_182d37bc_d613_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_182d37bc_d613_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_182d37bc_d613_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_182d37bc_d613_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_182d37bc_d613_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6250</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3197</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6063</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6151</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.4117</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4126</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6964</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.4870</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6709</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6767</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.5083</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.5135</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6429</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.4580</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6240</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6308</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4267</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4293</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.5982</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.2996</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.5846</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.5905</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.3648</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.3654</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.6161</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2728</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5855</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5881</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3727</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3808</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.5893</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.2870</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.5755</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.5766</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.3439</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.3482</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6339</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2910</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6312</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6168</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.4073</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.4169</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6875</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.4098</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6618</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6715</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.4922</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.4952</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6607</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.3304</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6628</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6532</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.4530</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4587</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6667</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.3860</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6605</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6586</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.4647</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.4655</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6417</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3541</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6263</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6278</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4245</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4286</td>\n            </tr>\n            <tr>\n                        <th id=\"T_182d37bc_d613_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0342</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0724</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0349</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0343</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0522</td>\n                        <td id=\"T_182d37bc_d613_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0520</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"# **This function creates a Soft Voting(Majority Rule classifier) for the selected estimators in the model**","metadata":{}},{"cell_type":"code","source":"blend_soft = blend_models(estimator_list = [tuned_lgbm1, tuned_xgboost1, tuned_gbc1, tuned_et1], method = 'soft')","metadata":{},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f982bea5a50>","text/html":"<style  type=\"text/css\" >\n    #T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6339</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3152</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.6126</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6221</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.4190</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.4206</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.7232</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.5067</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.7014</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.7043</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.5562</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.5628</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6786</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3183</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6504</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6625</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4807</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4831</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6250</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.3200</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6184</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6176</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.4013</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4042</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.5804</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2586</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5422</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5552</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3169</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3211</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6339</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3131</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6032</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6122</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4051</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4108</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.6250</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2875</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.6130</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.6059</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.3921</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.4020</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.7143</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.4327</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6888</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6983</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.5392</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.5433</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6429</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.3153</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6226</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6297</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.4232</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4261</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.6667</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.3424</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.6361</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.6508</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.4637</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.4648</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6524</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3410</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6289</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6359</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4397</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4439</td>\n            </tr>\n            <tr>\n                        <th id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0415</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0697</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0427</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0425</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0682</td>\n                        <td id=\"T_4b7c5ecc_d613_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0681</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"**Check the ROC Curves w.r.t to default setup and check improvements**","metadata":{}},{"cell_type":"code","source":"plot_model(blend_soft)","metadata":{},"execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"stacked_lgbm = stack_models(estimator_list = [tuned_lgbm1, tuned_xgboost1, tuned_gbc1, tuned_et1],\n                           meta_model=lgbm_1)","metadata":{},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f97e3bec150>","text/html":"<style  type=\"text/css\" >\n    #T_8847db6a_d613_11ea_8583_0242ac130202row10_col0 {\n            background:  yellow;\n        }    #T_8847db6a_d613_11ea_8583_0242ac130202row10_col1 {\n            background:  yellow;\n        }    #T_8847db6a_d613_11ea_8583_0242ac130202row10_col2 {\n            background:  yellow;\n        }    #T_8847db6a_d613_11ea_8583_0242ac130202row10_col3 {\n            background:  yellow;\n        }    #T_8847db6a_d613_11ea_8583_0242ac130202row10_col4 {\n            background:  yellow;\n        }    #T_8847db6a_d613_11ea_8583_0242ac130202row10_col5 {\n            background:  yellow;\n        }    #T_8847db6a_d613_11ea_8583_0242ac130202row10_col6 {\n            background:  yellow;\n        }</style><table id=\"T_8847db6a_d613_11ea_8583_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row0_col0\" class=\"data row0 col0\" >0.6071</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row0_col1\" class=\"data row0 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row0_col2\" class=\"data row0 col2\" >0.3126</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row0_col3\" class=\"data row0 col3\" >0.5981</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row0_col4\" class=\"data row0 col4\" >0.6011</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row0_col5\" class=\"data row0 col5\" >0.3875</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row0_col6\" class=\"data row0 col6\" >0.3890</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row1_col0\" class=\"data row1 col0\" >0.6875</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row1_col1\" class=\"data row1 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row1_col2\" class=\"data row1 col2\" >0.3371</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row1_col3\" class=\"data row1 col3\" >0.6517</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row1_col4\" class=\"data row1 col4\" >0.6657</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row1_col5\" class=\"data row1 col5\" >0.4918</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row1_col6\" class=\"data row1 col6\" >0.4956</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >2</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row2_col0\" class=\"data row2 col0\" >0.6339</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row2_col1\" class=\"data row2 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row2_col2\" class=\"data row2 col2\" >0.3004</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row2_col3\" class=\"data row2 col3\" >0.6036</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row2_col4\" class=\"data row2 col4\" >0.6179</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row2_col5\" class=\"data row2 col5\" >0.4099</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row2_col6\" class=\"data row2 col6\" >0.4112</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >3</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row3_col0\" class=\"data row3 col0\" >0.6339</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row3_col1\" class=\"data row3 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row3_col2\" class=\"data row3 col2\" >0.3150</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row3_col3\" class=\"data row3 col3\" >0.6098</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row3_col4\" class=\"data row3 col4\" >0.6191</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row3_col5\" class=\"data row3 col5\" >0.4111</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row3_col6\" class=\"data row3 col6\" >0.4138</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >4</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row4_col0\" class=\"data row4 col0\" >0.5893</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row4_col1\" class=\"data row4 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row4_col2\" class=\"data row4 col2\" >0.2617</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row4_col3\" class=\"data row4 col3\" >0.5394</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row4_col4\" class=\"data row4 col4\" >0.5556</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row4_col5\" class=\"data row4 col5\" >0.3225</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row4_col6\" class=\"data row4 col6\" >0.3299</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >5</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row5_col0\" class=\"data row5 col0\" >0.6518</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row5_col1\" class=\"data row5 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row5_col2\" class=\"data row5 col2\" >0.3048</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row5_col3\" class=\"data row5 col3\" >0.6400</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row5_col4\" class=\"data row5 col4\" >0.6310</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row5_col5\" class=\"data row5 col5\" >0.4290</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row5_col6\" class=\"data row5 col6\" >0.4354</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >6</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row6_col0\" class=\"data row6 col0\" >0.5982</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row6_col1\" class=\"data row6 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row6_col2\" class=\"data row6 col2\" >0.2762</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row6_col3\" class=\"data row6 col3\" >0.5740</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row6_col4\" class=\"data row6 col4\" >0.5796</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row6_col5\" class=\"data row6 col5\" >0.3497</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row6_col6\" class=\"data row6 col6\" >0.3554</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >7</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row7_col0\" class=\"data row7 col0\" >0.6607</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row7_col1\" class=\"data row7 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row7_col2\" class=\"data row7 col2\" >0.3393</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row7_col3\" class=\"data row7 col3\" >0.6427</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row7_col4\" class=\"data row7 col4\" >0.6499</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row7_col5\" class=\"data row7 col5\" >0.4564</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row7_col6\" class=\"data row7 col6\" >0.4585</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >8</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row8_col0\" class=\"data row8 col0\" >0.6786</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row8_col1\" class=\"data row8 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row8_col2\" class=\"data row8 col2\" >0.3376</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row8_col3\" class=\"data row8 col3\" >0.6553</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row8_col4\" class=\"data row8 col4\" >0.6609</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row8_col5\" class=\"data row8 col5\" >0.4812</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row8_col6\" class=\"data row8 col6\" >0.4882</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >9</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row9_col0\" class=\"data row9 col0\" >0.7297</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row9_col1\" class=\"data row9 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row9_col2\" class=\"data row9 col2\" >0.4116</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row9_col3\" class=\"data row9 col3\" >0.7173</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row9_col4\" class=\"data row9 col4\" >0.7198</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row9_col5\" class=\"data row9 col5\" >0.5630</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row9_col6\" class=\"data row9 col6\" >0.5650</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row10_col0\" class=\"data row10 col0\" >0.6471</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row10_col1\" class=\"data row10 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row10_col2\" class=\"data row10 col2\" >0.3196</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row10_col3\" class=\"data row10 col3\" >0.6232</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row10_col4\" class=\"data row10 col4\" >0.6301</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row10_col5\" class=\"data row10 col5\" >0.4302</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row10_col6\" class=\"data row10 col6\" >0.4342</td>\n            </tr>\n            <tr>\n                        <th id=\"T_8847db6a_d613_11ea_8583_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row11_col0\" class=\"data row11 col0\" >0.0416</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row11_col1\" class=\"data row11 col1\" >0.0000</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row11_col2\" class=\"data row11 col2\" >0.0392</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row11_col3\" class=\"data row11 col3\" >0.0469</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row11_col4\" class=\"data row11 col4\" >0.0446</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row11_col5\" class=\"data row11 col5\" >0.0674</td>\n                        <td id=\"T_8847db6a_d613_11ea_8583_0242ac130202row11_col6\" class=\"data row11 col6\" >0.0667</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Now Comparing best tuned model with blended model**","metadata":{}},{"cell_type":"code","source":"plot_model(lgbm_1, plot = 'confusion_matrix')","metadata":{},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(blend_soft, plot = 'confusion_matrix')","metadata":{},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(blend_soft, plot = 'boundary')","metadata":{},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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ke8KuhjtPBwrai88BzuxDdeEWIx9UyqQkpOwp6yuCoTXQNUf9YBWOfe2cBl5oGQm7WINECq0zEIGnpQD0fKjKTRdoXkVU/CbpqXOsGbqJz3/deop77XPndflq1s+NJn0ctPhnRyIFl6tT8LGBJwWYGZM6kF9wjZOnhTr2fDiqfJlx3FzThsKc2Z6OHgVWmYhi96PRpWsUq/yVazjTJJDoyIzE3YvW0KkTT9vmqmfWhsbiLanzqR67XPndfnqlg9NJr3cdLDSZarZXQxVvoZC51UvORQivBrG/CH1V/VH/c96VLRYK4uNVJWvfOmKNhAtEomwePFiHn744dEQX0YZZZQxJGQqf/lOQv8q6H2P/ifci2SVkUQ0FuWDn72RPz7x2JB5jcqkvWbNGsaPz1xowQ3F6rYstMyyezw/Gq8yi7nPS9E9LluCut6J57QzBcsdr+5xGxRgRsR0h2fb6y67x3Ws/91DjB1TlZOuKM9pv/7667z22mssXLgwr+vK57SHh5dXfsfTOe1cdKMhczTOaQ9F//p1W83/H6/ntLPRaTFfXrwGc+bbK91IntOubMpfZi6M9jltK15+bSet+1uZM20O82fN83S2OhfN3jc72Lt/HxeffX5BdB/xlfZ3v/tdvvzlL4+02KKHkfM5VNvjmpN8sBjqUbHhgKwIWzudecJHG85jXm65wDNeN8JtcJM5HHoMxzjSc3a7yxrM2Hc7nmf2XYGPkJXPaR9/uH/TfWxXHyW44C22q49y/6b7CsL3Jz+/j8987FMF4QUjvNL+3e9+x1lnncWMGTPyvrZY3ZaFkpnK+SwDAsicGzkft9/cn+XO2TzSNtPbmmpnJoyWe9yozmbALRe4Gz8n3UiMHzeZXvLC5yszW+7vwbotO6INjBGb02jm/kzPk54tRW8m+1v7zWoL6/eF0N/I5e7U8R3hHh8lmcNZ5evvr+6EmUeYXKNv206uGU8nR/n7npeZP2teXrys+L8nm5k3ay5TJ03xVDGs6Nzjjz/+OB0dHTz++OMcPHgQRVGYMmUKF110Uc5r3ynu8UK72r2gWG1W6vqPlnu8GLd0vNKNRp+Phnu/VN3jg5GZC8XgHt/zZiuTF9jjrCbXjKP1mdack3Y29/hfX3iW/YcO8NcXttN15C0Cfj+TTprAefPOHrTuIzpp//CHPzT//5Of/IRp06Z5mrDLKKOM4xOZjk6VUcZIYs60OWxvf9RcaQN0th9jwbSLh8T3rs98xfx/08MbmXLS5IwTtleUzDnt4909Phy8Sj36utT1P56jx60YinvTeXSqVKLHR4KXV7pivecKLXM4o8fnz5oHbSfQ2X4U0Cds2sYzf9bpefPKTJebpqiTq3z605/mAx/4gGf6Yk16UUiZIuojum8iasSHGsm8AjkekqsUMulCsep/PCdXsaKQSS9Go88Ho/87PbnKaMgc7uQqn7jykyzwXUV0+0ks8P0zn7jykwVNrvLJ65dx9WWXZ6UpOvd4GZkhoj5aVjQQ75rgad/GM9+Y/gIgKypSkfhVRNRHvLMK/6So7fti0VFE83fZiqjP9iZdLG0pJETUhxQ4/to1GOxZuUgv21rGcYX5s+bl3MMebZTMpF2sbstCydyzciGxA2FCM7uItI1JJ7ActTF4yYqKHExk5BmsPsqrqxYCMCdLgQdXfo6jPYW0xZ6VC0GS09pp6GjoEaxOVadKa6uLPYaql0G3Z+UiYgfCWY83OfntWbnQ5ubV29LnWeZQabYcbeEmWfOcECUfmcZ1e1YupOb2Z2zjSFZUgtWWMevSXyKmT/bB6qO2Pje+lyT9OoOX8f9M/Z1N/0xtlF0qfFnHWX/rOND0o1yZFk5W/eesa6b9zvPTsqI52+gG/UU6jqQk0OIWG2Cs4FN6mPzyGIv50DntnFH/IT8PnPrnl5M+X7p8o8cdv7r8XyK9DW70DkiYfSspCXCc75c0+1gPVh/Nopd5jUeH/CggGo2yc+dOKuLvxycf8xitGPYQVZqbptB0xcrrnSLzeNAf3KPH//igoP8+CH8S3rN0TFHqPxoy8+W1a+kSZt75dMajbYWSuWvpEuKdE5m99hHabr/AJtOpQ7HbbDB0LSsaiLRXmR4tX1jxFhWeB51UFeSy338RvyS5voTJQQ0RdX87C9V2E32zkuDUPuSgxkDbGLSYj1Btt34+39lGSUbTsh9fzUQXj8M/Wt+HTKdOI4epPfMQO3fuZN68eQSD6S8fJbPSLqOMMtIRiwkObIQP+2R+uVEQ+5AgUL6r88ZgtkTKGBxmr9mGpo1M/vZQTQ9yMH2i1eJ+pIDDS2lJlpMqDSsXXd30krm9j3f3+GjxeqfIPF71X/dVeG8UkOCqKDR9uZ+bv597r/WdbDPzd+MZLSK0WCYQp+/RWKll4mfQS1JhItb1sqAgycVns8HQiaiMtcxpi6OC2nC6xzO+FLh4u0O13fo2ipVXTEZWMjijjW2YQuYx92DXkomkKNao3tGQWer6j4ZMSQ6xYY0HF1YR6+9Gp7wMVcnIsCpJxv+yJ5HvmD7PBqO8qJPO+N74V0T1YM7ovoloIhl0KNLp89E/W5Rwa2MD/XtOSE7cxWWzwdC1NDbwUsMHzL9sJU+zYbijxyN7x9K/5wTbX3TfuLTvjD8R8YMmocX0f3P9ZaIzxpcxxnKhZFbaZZQxFPxhY5TQ/8IfxwmuvqFk3lVzIjYfel4QVEkyPZogcYbMhjWCZSuPnzbmg6FGuDuPcomYfqojsncsvrBi24cOTuvTV9lDdKtbV9YGWhsbmNPUjD+soEzvL0fsFyGMVbwkS2jCfRXd0vYqX/rPO5g+ZSoAp8yo5baPrzJ/TyDY9dErSXTqwZ2+sEL9+7LLLZlJu9hdQEOhsbqPoh0hgjOiGWm90hh0FbNy3/CGblY95KD9wTfaNhuKbvq+r0jt+14nUBR3ozjdeU5Zg9V/qHSZaFZ8C/7n/fDhGDwahJPnq8TWSzlfTkZSfxGVkQICEY0hKaDF3O0rojKRdn3ManG7S9VLn4uozO7lDdR9828Ep/USac9+n2hCQsR89LX4TVmtjQ06r5gPTUjmZ9Ddrsb3IuZj9/KUm1fEfKgDPk/3piYkGy9jgg7VdNva3NrYALLEnPseIzz7iL39g3DJZ7KZVWY+zxavdFb+bii26PEnn3iMS5PnqYdamnMgMsCiBZfwuWU3Zw9AT6Loco8PBcWaE7oQMlsaG8zCA14iJPOJopy99pGcBUMM3ax6zN2w2XbdaNtsKLo5933XfRVu/n5mmS2Nl2SUNVj9h0qXKfe4osicvFSw6T7B5Bs0Oh+UPL2cjKT+Rt9ZV6pu9m1pbLBFVVuLcXjpc2OvdPcyvQyml/uktbHBlc46WRvwhQLm987fjc/e782AjYebPKtMZ/t3L0tFmQ+ln6z3VX76F46uGHKPG3jx2b/y2ve+RlW4krPOu2jIpTn7BwY80eWje9npUsZxj8Hu+5YKrr5BJvIh2P+ixFXJxY/xclJGGWV4g6qqvLj+v7ms9wgvPvDfqGp61Hm+6I8O8PfWXdz23a+z8o7P89wrLw2ZZ8lM2seze1y21ObN5B6RlARyMJFMBtGlH8bPUs9XUhJmogqvuhmH/OVget3k0bbZUHSLzYeeZORQjyZIzM8uM5usweo/VLpcuceXrZTzejkZaf3N8ZijBrUx/o17whj3gxmPo5HTeqRk5psoxytdZr00JBLmnz/aZfsskUD3/Rp/ufh5kTl8dG4r3kcfvJ937doBwLte2cGjDzUN2T1+anUdy6+9gf/60l18+ZOf5Ztr/5N4IrN+Zff4CPAqhMz6dVvN2tmZXECz1zxuHkmQ/alkEJlqDtc3bSU8K5GX/vXrtmYM5Bltm9Wv2zpoXs593xXfyi4zmyyvMr3S5MML3JOrGEgLSsvycjLS+hvj8ZUbLslaJ9twD85e8zgtKxqo+fozrkdxCllmshRLc1rH6EiU5qwObiEop9zzckUYMeDOqz2yhJg2Lis/LzKHQpcLbq7qQ09sxigPMgboenwT0r/cNCT3eO20amqnVQNQM3UGJ447gUNvv8XUSVMGrXvJrLTf6WhtbOClxR/wnIxAxHxE9lbhIVFP6ppk/nPjuMnxAn3fN8AmVTB1GRn3eUcbXo6kZcOKb+kvJZD75WSkYYxHt0hr5xEqgJbGxUT2js173JdRxmAx8bIr6E3+vxeYtPDKIfP8w+Ob+d9N/w+At46+zdvHjjLxxJOGxLM4n14uOJ7d49YIzkKV4mttbOCVj13m6WFn8LM+KJ3XFZvN8qW7+gaNyIfgqo9mH/Kjpf8fHxT6kbQH3SfuXO5xMF5OJE8vJyNt/2zj0Rh3UNjSkMeze9yKkXGP2yEiuXhl3+4bjMzhdo9fdcMneP60cwF4/vRzueqjjUN2j//TeRex/e87WHnnF/ji9+/gCytuIeDPvJou6tKc+aLYEwgMhcaaIaiQpfjKpS3tNF7OLhu83FZ/g5FpINsqOp5QOLARrvTJHNioH1Fz4+VF5nuWVnp6ORmNPh/p0pyjkbTjnVKaUw5l5lUT2oIidXvmVyzJVXw+H2ct/1eeGHMCZy//ND6fb8ilOcdWVvGfX7yLNbd/j6Zv/oiLzjp/yLqXzKRdyhBR3TVoZL0xJgMjE04+yRkMN6PX64zSnFa5mXQbidzLRput9shkF4PGyELlRjsUHdzkGjKNbYJMOnrlCeRcRTd9JVLQqO9CJ1bJ9gKTa2zn6icjmYk1qYlbrWpjHB9P2zalBA0fwvIX06psn51/MPzPkuHAWeddxKwvfJMzz71wtFXJiJIIRNM0UAf0xAy5XnyK0T3e0riYSPt4SGbNMRIpOHPwQm73iJFwgQwZeJy8rOc/3c4cO3XLhILaom2caxsM/Zw0c5qa2Xvn+TZbWduST59rUWhZvphIeyoYak5TM+HZR9Js4XZ21pDrlGnqbKELTusnjqOgh8vZaeXvwhH1nT4zjeb2REvj4ozVr5w2s/ZhdH8l9eubTVq3sZ127lmOu9pd/05j7oYtrvZ3Q9k9nj9dJnkdUUefRL0/gwpBky/dUJKrGIlVctHlQ+OV7rhxj79y/VW8/J4P09+ae+VTbK5es/ycxe3RmszF65ywoYjdSSPgKjVWU8Yqy6BpdXm5yVemQdO/CsQ++2/WPfxCuWd3L7uCl9/zYe6/1ZdzFR07Q855JM2re3w0ticy2ay+aaut9vZQxrakqMz9+WZLycqye9zAaLjHS95mHtzeXukKycuL7iWx0jbg9gY+p6nZVjot0VWFf1L29HqZaGRl8DmL3WBEY+t5iwvHd9D6GJNiEs5jNMMuP0f5w0xZoQYrS9O896mbSzYTnTVPtHFELhNCr6k5V9GN3w7xs/f2ezqSVkowbFUI1Dc1214AyijjnYqSmbQzuQ3SHvReXMcZaAy3tYGh5uG1ur+LwQXntNWcpmaMPMi5MFQXnJEXOh97DLZ8oVWW1Z0topmL1Vvds9lg5ImWZImKWf2m6ziT/pH5Pnpa1axnpwP+GCcvhU33ZY76Hu3ofSO3tpED3Orps/aTdaI2bJXPyYhy9Hj+dKMRPV7sNhuKezxfupF2j5fMpD0SCRCck1qx5tctFK9UvuTc/JyJGfJNwuLcvx9qO635mJ0y3WIFjJzik950n2Dz0cvIVz177SM5eS39XCW/+lx31lW0JIe4+oZ+NhwTXJ8h6ttLchWDzknjVvUr3+QqxlgxbK6XozTy5adsZvSLAes9Vcix/U5PrmLFSCRXGU66YkmuMli6QvIqR4+XMWywnq0dLXh1vxoRziMRHe+E4pf1VbSHs9PDUU4zV+R6GWWUMbzY/NSf+JevrOLGr/4rf33xmSHzK5FJW0u6drScf17oCsnLK11xuJMGr1thXHCpvMRDb6dGa/LFwV1m6o22pXExu5dfok/cQ5KZH51BYxT0yHR2uhC5x9146SVJ3c9/53/KItV3IiqDkHAfP5rLNV76PL/7JLf+1vsyO4rj3hw83UjlHieZfxy0ZHKV3M/GFN1gZA4f3Ui5x4/1dLPu4QdZc/v3+d7n7+DPO/6Wlddx4x6f+/PNiM4E/kmHc9ImuibkpPNCU0i61sYG4l0TRt2dNKdpqy1oL5tuzqA1Zxvn3K/nPrbSGHTK9E6b22cukgEAACAASURBVFxEfSa9Eak91HZa2xLdNzFNN0OOiOmyDf0HVqmwZ3AynXRuZ4zdaNSIjxuW+1AzPDPdbOaEJIeI7K3Ka2w3fVXjfVHNLEl6/5clbvqWL5m/PoQ6EE0L1rNue0iyToMmMadpK+13na97NzSJOev0o1zO8TOnaSvK1D7zmlBtt6c+N/hnuk8MOxq65nL1zmnaij+s8PpXzsrpESp2V6/aH0u7H60Y0nPKMW4z6TUjuBXFkntchCYgR3LLFKEJ7D96FjEtcx8Um3t8b0c7tTNqctJ54fXszhc4d95ZVFaEGVNZyZc/8dmh6655feUYBUSjUXbu3Mnr77sX7VikcPvLo1Eztkh5DYfMWT951PZy4JYWdbRsVh3czMHYBWZBg5GQ6YWXte65M/JdRH3EOyfQ0tiQl8znfL/hI0pqdf1QTOZc9YPMaWq2TWjWM++7li6h5mvPEp59BNkfzlngI992DtZmkqIiyfqZ/YraHiQ5nDZpOwvoFPt9UowyS11/g06qCnLZ77+IP4MzWZIlNJdgZCEEn1y+gvvWr0OW5Yx0XngBbHjkfzl4qJPu3l4OH32Lxg98jHPnnW3+nkDwxHvvJtHZY+r+/sP3snPnTubNm0cwmB5QVxIrbSh9F1ax8spEp7vBUvBHjyIYYydySXYTrD7Kq6sWpguRNNtbfSHaaejo1E2zZGRKp9GQUC3/2umzyZRIoOFDQjX5GZ81lyxQaiSOpCSQJH2lKAVUnSQGmpZZZuvKhUT2jqV+nT5BtTYuROyTCVS/hXizIqlL6piaLl8GUicBJEXlSOIkekQnVbJMjxAcUSchKQleXbWQ0MxeIu1VSJYSj6C/KLy6aiFz1jVTUdun/+7oOyec+kuKihaXzWuerXqT/TUDTOuo4DymZuTjxstAfVOzLblLqUVCG2PHyzhz42eMIwN63+u2DlYfJdI2Bi2G7onOkITKoLPC6SUqJpsNhS7f6PG3jx7hoft+hhzXOP+1Mdz7nR+hBiQ++qmPc+K4E/LiZf8Nut4+zLdvvZ2Db3Xy6W9+mYd/9LOMZ7aPG/c4lIYLqxR5ZaKbEdyatRSfwMeY+1V8tamVIWR2jxvn1fPRLReNoaNVN4GPjkiD6Y6bHnycoHwEn4WmOrSV9sgSpihPE5D7bPTZZE4PPk5n7HymKH+jojKBGOinPbKEycqzdMbOT3MB+kIBZv9kE6HabnY3LmHSgac54Z5uIt+Btj328oUGtChoUR9a1A8RCRHxMUPZijQuzGud16JFY2ZuAsODoU8GdptpMR8LxSU8Iv2eGxSVRxIBFomLzRKvia4JKdexSD1AZq953OzL6L6Juh45AvicNtNiKX1+/U9vcM6K+Zw5cwL72w7z63Uvc92f6zzzyoRSi4Q2xo6XcebGz+g3AwOrVPbuWcLMNc/iDyvm1o+2Dyp+6kOqSefl5h53esGKyWZDocsFp0v7xPEncPqZ89mzZgs1iXG0P3uQ+pVXcNIJJ+ZeaWdxj584bjzzTz0Nv8/HjCnTCIcqONJ9jBPHjR+07iUzaZdRXOiILOYU8TQVdNtSeDpdvTCykeYdkcW2ybMj2kB1cDMVDs9BJvqsvJ2pHHN8D9C3ChIyiAig6BnZsqF/FYj21LWvxxYzRXmaisqU/l6T0PhlP1K8nk3STuT4XPyy33LML93VuGGN4Jynltj60stDMhOerdrPOSvmM23mBAD93+XzePaF1zivJ/uK2wnnnnapIdsY8QJnn1cHN5vfW7d+grJ+P7pt/wy1P493XHb5u3n6gT9ytC6OkOCyyxcNmeeC+e/im2v/i6XXXE9Pby/9kQjjq4b2LCyZ4V/s7phS5ZWNzogBFZrMwEAIoekraqHJaBkKAoxkOzV8CM3HwEAITcsco6rhc9Vfw5fWDu/6x7LaIRsvp542uiCml11SMO9QNRIblM3OlU+jPVbPOfJcmwbOyGrjaNjTR17PqX8umQb210TMCdvAtLqJ7K8ZyJtXa+NiXlp8rfnyV2ru8ULTWceuQWPcD0O5N49nm1nhtjJu27eXsefN5N82fo+x582kbd/eIUePTzxxAovOv4RPf+vL3Pbdr3Pbx1ciy5mn3bJ7fAR4jYbMkdC/Q2tAsgzGipkqA29YoleDmHuhsiKQQ/pKsKLOvkfq/N2ai32o7TRWL76wgjrgTicpKvulhab+M6RmDkYXENeqXFc/bjL/JP7Ou+UzzM/7olfgCysIMYAU1JBJpKVolRSV8KwElf+uUVEL/hWCf7QvZs79zbTfuYBEeyUy6TarXK3TJtoThO/RmFe7hZYVDUi+MJVE7H2QhCHbkNm3W0EKCKQkz3dH5yMF9H1mKSCoX/8Y4TqV3TdeRKS9iphQzaImD/a1IZR5+GWZijrdZnpEeYqfEwadEzPeVNjfdtg2ce9/4xDT2ivS+kiSNUTUhy+kIBIDabLmNOl2M+xUau7xQtBZ7dQRbUBSVGQ5Ydp/Pwv1i4KYY8sKt35yjttMehlxIAZCFSqRPvdoditCFSoDfQrZqn4Vg3scoK5mJqtu/TSA+W8hkqu8f/FVvH/xVZ6C2sru8TIGjfqmZoLT+swoZtmfHq1r0q5LVXJyi+q1/u6M8B1uGAFMhl59jTC9fZttHzsbdohd1Cgt7Ij5OVc+zZU3pLervqmZ8KyEaQurDaz/h3SbOX+vX9eMJOvJ6936wJBtyHzlhktsVbl2LV3CzDufpu32C/Tva3uAsCnnns8L3hsFJLjGl+D3Z/+Wm78vm3oZ17tV+XLT38BZwF33HwYuZtrMiex/4xDPrd/JdT32PW3DjruWLiHeOdH87My657TLOw1WOxn9bR3bueAl4j4TZgSb7TEuwTDCg0w5GKat/5KMpzXKyB8lM2mXujumWHllohMxn5m/28i17cyvLgf1aGgtJpv5paMdISpm9TsC0VK/D0ce6lw5ytUBn5kfXtNU131sYzVt5ZUQCbRAC1cGJB7UWkjEZ+OX/aZMg7e1XVa5fS1+0xYiKidXq3Z7QLrNjN+NPN86/wj9e8YTnBFFDuqrZqdNDZmakNJ007PHSWn2AAi87CgN+ndht5mDnxPZcvR/9WNtPNA4lpeqYFpHBdcdSw9CM3gbefCNzy2NDdTe/oytHoCZ+/wd6B632sn4rGcE1G3hHFfW8QPpNnPSZ9PLcL0biA6ECGge6jIMhLJuIWWTORS64zn3ePmc9gjRFSsvL3SG29XqKjVcrU43ZkWdSvW/b7NVZGpZ0UD/nvF2N1wRndPeIXZxgrKTI7F5LBhzlsnrMZ5gWfCgeWRqQ3QKl3OZyU8kBkw7WI84WWUaQXktKxqouX07FbU9eiR9e5Vpy/CshM1mxu9zLJWtJDlMS2MDA2/4TFex06bWdlpdqYbr3NpnFXWq6Ub/69zfcq1R1EQINsYns0S5xKTJGT0+gmM7lW++fE4bdHsYHh1j3BiY46iM5rRZvvel1UUeqky5x92OO+bTzmI6pz0YuqHwOq7PaZcxerC6etNcrQ6XaSb3+Ei7xb0ibTUt5pmPnxN9h6lKBo1UyTIn+A6DpQpnfVOzaYe22y/I2j6ne9w4Blfz9WfwhxWU6T2utDYeTc02d3s2m7q5Uq19Jslh041++nSV/3k/elGTEHxuUyeK8luTphj7rYx05Lt9kO99aXWRW93j+6INRMV4su1bl1E4lKPHS1DmSOuvu11DpmvOcLWCnjxARGXUAd1119cSSivi4ebLKRabbZOf4hq//ts1/jjNkW3mb2+rE+gRemP05CSpgCrDjet0L2aSqWkpO2ha6hhca2MDr3zsMtuROIPWSq8OxFAHYmm/5ZLpBk2zu0oDAUtRk6X6Z0OmF4zG2H4nusclRUUKJvRkN8nPSFrauMj050bnVS9jlW2cKFEjMfP/M4LNBKUjSCTM3OT5tLNYoscHS1cuzZkBpRLhWWq8vNBZz4IanwF2L7sinZfXNKZFYrNsq+lF4mIeSdiTkxivub5QIKsdDBoDu5elgrmsZS2ddAZtpG2crQxmvHMigOd2OnUyPu9edoXpUrV+vvqGbjYcE5z+6JW88NP8zmmPxth+J0aPW5PWGJ93L71yREoIa/jYF73SzsvDS12pJFcZCt1Il+YsmUm7jNKB1+QfowHn/tvb6gQ2Rg+wNOijRwiOalPM39ySkwwFXkuJZkJz7DkWMX9wshU/UlxFC/hQ5RA9TEqmPk1h2UqZXU8NXr8yigNC8YOc7gEStRPhjS7bd1I0YTvaWUZh8cjjm9n0l63m55Y3XmXrut8OiWfJTNql4sIqNV7vFJkGjfNs9jgxgRP9B2mOC7q0AAvFRbbfz5VP40+xBO+2JSfJLtMZiW48E1sbG5i7YXNG3dyenUaE7/b+l6j272aH8KUdPfOiV+f6mzjpa//LW9/8EPvrvo4USzCJe1A4ZNI45Rdzn7u5xzXFjyaBFvAjxRNFrf9g3eOGa1yLpQIMA5P1nPTGd53rbyJRNymNlxRLoCn2R/7kG/4bpS01Bo4XmxVL9Pg1C6/gmoW6l+uFlr+z9W9PZuVVdo+PAK/RkFks+hsRylZkSrThNYlDvnoNhVdCJJACrfxzQGZjTEB8DsExFWl01sQqbvysBTLMc93Cx2X1s2j95GLzGjmUSH22FOHwhQK0NDbYbGmlTQgBgV1c6Zd4UNuN8M3Cn3TpZ7Krs2+mNf4IEfUxdfmPzejxgQM+CGEmLGltbCB2oNKWKMc4MZAtgtwtAYgV1ih5azS6hAqKZjuWZB0/1sh3k2fW5CoSWjCAjyhSUGQci04Ukm6ovJyJbDLRScFURKSvQkGSUt9Na/xRfrqFstNoUdLS+OWTXCUS92VNPev5lEIo4Jp4x41fvLcCSdKQJHdaOaghoil5bR17eOX1bbZCK5oG8+cuomby7KzynLwyYf3vHuQbq75g01/SNOSgak9OlQMlM2mXUXxwVl6CzIk2ijF6fJv8FMv8cUDmfX7YoOauD+wGIzK79/VKMxL9F/6d1K15g4DfPWDKLRlLpuQl93xe8N4XAGSuCaSSnzj5SIpKqLaHvt2KKz9rkpRcSXAg1ZdOGW7H27K1w5AbnpUws7ABzJCaGX9/D77adJnGddH9Y6hf/5jt6FImdD5wEz3SqSxSr2Esrw0p6chg6YbKy5nIZiRk5qLpWwXiVWwTdz7JVUL39tv62E2mdVxkQqg2mnacNBM/VVQRqltJIMMWsSTJaJa9qgknSMgH/4dzz0w9A559cQIn1TdQMfFIdnkOXm7Y+crrTK0dx/TzJSDFLx6H0x7ahEynqXsulMykXezumFLl5Y1OQ0Qi6HvB2d8ovUT1etVtuG3mFoQ2FJnWl4Cr43D/FwcYc6rGspXZD2k4eVm9bZIEgZexJz95WTjo9P/UNzUTmt7L7hUNaXycyNVPmgZiIKbnP7egvqmZvd88j4E9J5jn091sYci2lZOMRJjTlHox6P9Ebr3qHWeNvervmUYDLRKB3PE/pj2MNjntK0m6TC+eVzfbOvUCu27OVWCKWSTtO7fKj65bCg5d3WwR/qneV0YhG12kt/vcoMtmEzEQs42LTNBifiQlkdO+YiCGlsW2gH5e2mKjSRMnEBX/hKb9xgwYi4p/YuKEk8hc1cCdlxt++7vHuebqf3K/3lI3QXgI7iuZSft4dC8XAy8vdDOCW/GHFfYfPStn6k8vUb1edRtum+lHug7a6k37wvnLbGlsQIvL9pcASebNZzTOeAH+OE5w9Q2ZJ2636HHrCjMiQ09cmHpG5/gAjf5bQOyD6uAWDsYuoO8TICpDiCO6TvXrH6P9zgVmohZZEZ5zd7c26vnOTfd4EoYbXw4atcjdbdvamEomUxPcQuJfIVIXJtHWT3g1vHpHAxP3bSeEfUL2On6y0QmCaEjIHnj13wKyHOKNlouIa/bEJOKuHvbubWCKtJ0DkQtRKybaKti1NurJTKweA0kO0XdzP6EvQeS7+kQXXg2RO0EcgPAaaP13/TTAdN8jHOi/gLhvDNUVj6FIPYgePaK/pmoziq8b/8yQaTNfjUXvT6T4+Wfq7ey/JSXPSpvNZsY1xAAN5IqQrQRvJsgh73Qtyy4iJjKvoo1ELdmStED+yVUm/H48cS/JVSQNSYJLTr2NZ57/C+ed08Wzz03m0vrbiO4bl9P17cU9/uzfWrn1w59hYI/9bSKhCXbfcCWJLn1MVdSpnLIje/tKZtIuY/TQEW3A51NQteOrrJ/rka5BwHCPv92Wegl4K6EyRZa50ifzy42C2HUCRfGeFsG6wvzk/8NMfvJIIsDSz1YAvVSuBnkp8Gbm67PlPM8qP5nv3Oked7q/jXzhzqIqNllBY3LBjJ6vX9dMX6NndfLCE75fs0i9hnFuhnGgcjVIMpwums1c51pc/7cfvb2Ru4DW9GuNNjrbUbna/q8B52QaXg3y7VB/ZzOhaT36pNkI7IXwDyFUCxwCJgIuK0cnv8rV+v6zCARJIOl7+zlWiaHV+gtOpDGK1j48UeTVwezjznC1G0laJETyZIP7RCihmr9b/z9YVNR0IwVVqvHzl1cvQYjfEJMvpXqBH+LdEMhx5CMuZ6XpOnSEynEBxlQPQKDPfmkc5j6wCVnT3eMcyu0eLydXKUGZxaa/WwIH5++FllkIXuaRrrhmHukaikz9JUBfNd+bkLnWrzf8qiis+6o9WYoX/Q06RZE5eamk60k9iiLZfgfYpb6d5BVLu94toYbhxnXrG2tfuuWn1jTY/lfZnIC397+UDL7b5Sov/FOQq9OTe7i22cXV60ZfKPe4QWdNeGOt/y6isinbGuXv1o5MCUycNFZeBvo/B70f1D0nAL2rfPReC33fiOnfd3hrZ//n4In9G9kk/5VeZua0x5NSkjY4E4KgTYrpFfyMvwxzYT7ucSMJi/MPBVOmQGa6so2A1MP0oP6vE4bNrL9b/28knwlMfisVaS+7/0mBhPl/0QHidf3v3XNWcM+6ai6ZfSvqaz7Em5r5W8a/HDRdLx7lhNBYd7p26L9J7/veD+r9nQsls9IuZfdyMfMqhEzDTQjuuceLObmK80hXPjKN3OOGe9wvS+a57hpZoip5VrZKkpF3SETax1BR25NmD7eECkbSE2NV+56llfzwF1O4rG82oD/ZDT4Jv+BXtDFTfTdjTxHEdiume9yQV3P7dtq+dqElEC1Ey40XmS5sK4z+dIvq3b3sChJC8H2e4AtcBgi0wG4zDezLH3sv83++lbbbL6Dma8/Sftf5Zlud7s3q4GZCOAMZdReu0TYjKYzVFlY6N8haBAktL1d7y40X2eRZbWHkqfdVpKL8jfzvhnt8b+MC4lTZoq9nBJtRjMknBi3LGoj5q9DiPnwVAVvCGyc6og0QBV9Cz3Ffrz1GhXUrwVp33dpOCS4T1+NzWfi52WPyx9Yyrq2KA5wJnEkonooK19OWugd95eMebzuSocpXsqy6L2ZP1OJWMhdS96b1d+v/jeQz8c6TkKqSJx4yBExqcT8k3eNRUrqNHXsiS6+6h4njJxFVQdI85B4X2Wnqqk/k+184h2jMPfd4W/fVJI4mc4/HlJyZGEpm0i5jZOGsn6tIUSLY951EzIca8XHq6m0mXaJrAv5J/aiR1D7oqau3uU7cxQK3I11eMHvNNtrvOp+arz9jTk7GS4AqH0ntl2sC9V0SoWr9xpyzppndK5YQ2Zv+ILMdr4mBKoJI8RhSF3zmpwdh4humqzT41S3c2ziTitg4PsQcHjnpWeLdMRZPns2F/7GL0JQeRLJ/ZNS0eJrZa7YlU5piqTCW6s9E1wRe/8pZZr91ih428ypB/HyIM3iYnRziCJ8iBvi4xh9ng/oUpyfPC+ebZEdEfcQ7q1Am9TN79TZaLGPGGGuyYq9c5YQUjXOZ7zqqpNeB3K5GLQpaF2nyjD1eSRJIWiI57qF+TTPB2j4kVGav3oaESv8qqP9yM3I1untzYvpk1r8Kqvc1E14NrXcsId41Me0eg1TyH+M3RYpSu2abOXYMncP/lfy/pe9s30cAhay2At11LSzlzb1GhecDSRJJF0Np5CY/pfrU0VYhK0pm0j6e3cujySsT3fTgNhQptbJJiDH4Q73sizYQTwajuT6UZcl8gx2qbsVuM6P9Tju8Wz6DhEiY++WPBuHCN5fQv2cHoZpu+leB2i7ZeBllF62/9a7y8dLahzjt219G2acfRQloUX0vtBom/IeP09QjBJjHKfJJ9B+sJSq3MJkxtDY2MCPYzMHY+cS1scwINqPGJFs5x5bGBlu6VBGVzXKsQFpfTparmCHGchKVnCKfRJ+IgfQmpyn6hGZE4Lc2ftCTbTWSQW7JFVdLYwPRjnHMCDyW1LvKpGxt1M+sz92whdDM7owR35NuvAf5zm60OglcIrklybJFEJPpu1ki1hHgzcilSXmaafugBNOVPwES+6INJCIavat8vBZrYIryDAdj5zNFeYagdITelfq+akIK4E+WsJRQ06K4e1fKqFEJIUWYHtxGUDpi25PV768q8/5LiDHEbuknYNm/7l+VcqOHfwpCjiDPsH9v/CZb9rzVDp3OCiFkhGU1GhvQ9XfT3XZdHu7x6cqf2Be93Bbol84r94SeD52vSsHiiE9D8eQet20YeLJryUzax7N7eTR5ZaKTFRXZlzroH54ZJ9GmIksqspbIyCtXcgkjYUap2sw4p+wLKbZED84EJIGQRPCEU9n01m4mfiBIaIfGq7csTDEKAlHNtMWelYtSSReCIJNgPwuZcPMGujjdTLow8IaPGbc2czC2gCnKdq6vOsp/HzvKAa0PlQSfO6OPvXv0PtrPQpOXHEwgyyqv3rKQOU3NhOtUZEXoSR2ScuWgwBdSXZOrGAk/Folq/i/6Bge1HhKSYLyk0CNSke1HtYnm9U44x4YcTBC5zZJMIqpSWR9B3p+gtmIz9x2tYSHnMyO4lS7pHKYEt5P4dA+9YEZVWyHWAsEAT/h/zaU0Mk5+k76b+82jSkbgVv8tJKO7BfwDQhVxapXNiLgPOaCm5oUYSMmN++pQMyI0Ab84Qm2FntWutmJzakWOTheqiCMGMifIkCVBbWgzckXYdC8b1xpyJFRTBYPfwK3Y9DLQfyv460IkWvvt85MC/bfZZftnutAhbBlPc+lvtiMP9zgD/dSG0jMBmjQWW2TllQ9dqIqgdBOBTHO8LIPwkFPYC90QeMU1mBl6FCo6Td1zoWQm7TJGFuHVUFGX+izJuivudLJHguZK4FCMSVa8QlJU8xiV5Avb9u6dCUjq1z/Gu+pU/uenGtevjIOL3YzkIaHanpxJI4ykC4ZtT0ry++t2iZ41b3LnvW9z+6dO5O8fF1y4wL2PTrLpEHaNJrd+Z/SltRb4X7dr9Kw5wZR3VePbPPrt9LKemdqQbWycTipi/Y8PCmas3ctzVCJLC11qQ6ci0Q3IMnQ+cLP+ITnvOCO4rd9VrtYTh8gyhL4Ar96xyLbH39eoH6siDv8Qi/CfHE7rp75G+/nloeIf0UVMDmy37yUrevS9sdK2ygyvBn8d9N5sSYDioDcgyUk6p77JF48ySgMlM2kXu6u0VHllo7N6czIlg7C6G610GRNB5KFbsdmsvqmZUE0Pc5qasyZ6mJ3cg1QH/Cy9Wd9U3LBGmElWrNfUNzUTnNZv46XFZKSAsHkCjaQLzj648HyJC88/Csh8e/1RtJg/Z5INSI8kturnpLEmvrhogcRFC3R533ngKJom0/0RjU3rU2U9M8FrxHcsJjiwET4ckPilspM5D+0mVKXZErZ45ZXNPQ66C1mL6WPWaKft99UQuQvqv96MdLJ7nxu7tRqp6P1cTlynG9RgOSN5PMr4rEZiVN6nb4dk8sKKSMSWAMXYPnFLnBL+qePiGPStTLnVB6t/Nrpc7wPWEw8Fo8ueXM3TFp5nuiHysn7rpY0lM2kXo6t0tGSOhP7OiFa3xAbGXqiX6PH+PeMpttzj+dJZbWK1h5naMwljj9sXVpi99hH+9PRRQv+bSrJitcfuZVdkTBohKSpCViAq8FfoG7hGXvH69Y/R9rULibSNs51VNXgZNJlW79ZI4j8+KGz6OWmsiVKcaG1s4FRfmL9e8UfmNy8icpE7nVNmJkhyiHVf7eW9EUCGqyLw46smcjmXAakx55XXKzcuYqB9HFI0wWn/s8kcr0Y0faRtnKv9zfrRH0sGXn4sQ59rEtXBzQTlbjqiDcgVYSaq2zJGXRtwupc7kmeUndOlL6wQXy6orXiUoHRM/zKpqqoE6blFIjZzFkrba/qXQei5xV2mjS6JfdEGe8KkmZOo3v8gofihrNW/8nGP7z1qT67iTKLiucxnHnRSJEiNNh5V85BcJQu80A2FVwLB3sjVJAZ6TN3PzMGnZCbtMkYWzvJ+Rlk/t1J+bi5V5++l7BbPBSO5irV9Rh7wmFD1VaMlyYqbPYyiGCpBiAokTePUpifZPms9lcsfQzP64o0uTm/aQkVtj57hTNKYoTRzMHYBcW0MitTDAErG9J9OmKvaLElgsiVlMZKwzBEqblsAg4HyMhlrnOcLIx/55BvvMcfzqeueRMZYUaqmzawTybSxTxJKHGbfwOXEtTFmGVOjxOnspidpv/0cYm1hjLVSdbB50NHX1cFmM7mIeTpD8SNqJ9L59WuZXf0sVeiTdl8jxA8Eefbe39Bfcwp+WSLhYdLwQueXJfaLmzj3puuobH897Xc5Fs27lOe0qidtLh/ni4JbyVA35EPn7x1IZl3OcOQr6Ido5ticfOiy0fRHBrhr9ffo7u0hLlRWXHsDF5x5ruViEMEAInnsU9ROzKlTyUzaxeYqHU2ZI6H/4bU3Ep+ZGkCVHW30zZjJ5BvvIbDXKOWXfvNmd1umjn2Mls0kEpY3fUN/KY3OHSn6FJ2WjMi2Pxxmr9mGLyR44Cc9vDeqizCSrNz8/XTd5ty3jVBNN0/4fkXl8scI7D3ME75fkZAm0b/2Rir3vUFMt6Eg/wAAIABJREFUjCVWPYnZ/u1UcIw5a5ppWb4YDup6zQg2E9R6aZdyH7Uy+mndV8moX6a+3LgRli5N5+VVZi6a2HzoeUFQJaXSyxowjn7FDwYIVgNxIGA52hSNQ8CHFpUZOBCAqQkkYnStWUFM/B9KpJcn5Y2c4/8SYLeZ9Szx8z/ayFnfWQl79JMUB2PnE4/IdDV9ipPu+A1/TjzESdpvqAv+InUWm5TbWGgyEiJtzhCa7hGJDoRQtH4kKUVbHWymPbLEnNC61jYiyxpiShVEROpu0+D5H28kMnkqcmSAcGcbvZNTiVSEEtQ36h1wnbCjcdskLB08hjRlHM/98EH9M6pt8X/eTddRue+NvNzjO1b/mv6aUzLSBPYdJl49ISevfOjkmpOIz5yElqFiiFuZ0sHSZaN55Je/YPrp9XzqXz/DW/sP8LnPrGTDde8xf0/E43T+z0qiSS9kYF/uokUlM2kXm6t0NGWOhP7n3nw9QTl15MsWuZk81+mWlTOT2/K0jVtsq8vRstmM4FYOxi4gpo0zc3ZbH9bZeBn0cW0MoYoEA30KczdsSbpr7Q9EWVGpqEug7HQv9rH5rVYu43xTpqyovHrLIs7c93EORC4kxjjerb6Xv2kbmXvzbYzt1FNyioF+wvcDtfoRn+ldW215DeVQiLn3bclaVQlS/aRkKEZipbEiElH51U8ncN11hwmFfBnpssnMRbPiW708sMTPR/2CeyIB3qNdbLZRVlR8IRV/XVwPRHOUTZ6yfC2Hv/khpBpBRV2cKTfcY4651zmTuRsOct5/XE/o6wCzdJ6hkHnszMA5N3+EoHyEmtAbul5o+EIBpixfa8oBkIL2o1GG21iW3KOJZUnQHlmCWjGRaeIRglJ3Gm11cAtBuZtTV/3adt/1WmjOu/l6AML3g79O924ZAWrb1/2evpnpZ41dV9rBgO3VW6ubhCY0zl9+DWP22l3ptnbk4R5fsPIDad8LfHRE9BV3PjnF88k9Hvj9F/F7yT1uQSKRYNvzz7DoXefj9/uH7B4/cUCjrf1NAq0HGNjfzvhgmEDrgdS1ksq0f/mBLfc411+QVV7JTNpljDxUJQiSnsNYrgHR5iAojVwJNlj303IVKMh07fTgNvyKwpvqfLS4zDPbYYyDVsR8RPZWEJvXS8+LyVWjJkjM1/eQp/fvZYcYw7nyaeY19eua6VsF7MTmDhZKkL4aPZS/ou0VPT0jpLJiOQOOoj40kT0JiQHbqjapnxue2q5y/xdPJqwFWcqp/NuSV+mTonzy7gNccmFuOV5gJleZ3o+/ciZ/6N7DK9qJvKfQ2ZYt2cQ0fMS0CpzjoCPaQHVwMwHZyBWdwc2KL3nKVn+BSWhVyESTV6i2qwTGcbfU+Et9h/mb9XuDn6zYV7xqshGqBAPUEeI11CCIIGguq+x8IZQgajCU9v1g3OPpbcScsIsJj7/0HH/pOsiYd53H9j9t4tLJJ7Pw7HNzX5gFl1+4kEf/3Mz1t62gp6+X73/+DtvvwWm91D/wGFIy93iiK7cnoWQm7VJzL5cKr2x0z/94I301p3CZ7zqC8X2EcpS7A3cXqBn1a+QD1qRRs1mmFIheeBn0HdEGiEu8dPXDiG9fyvf3vsjXptpXS3tWLiI4rZ+PfqOXX32mRz8SFYSl39D4xYckPpxM+5mIz4ZI6mkc/inIy0Dar5rnp5+/ZyNjeQMR83Gu/zZkTV/9PTxf8AFNRhgLIkXP49x6y0Lq7n8m65620U8rvpUqRvJoUP/spAG4eIGPP8xIMPv12Zwin0RvPMZrs3Zx8QJfwdzje1Yu1P3cn3iKnX0J4ok6PsIpPMxOIiLBFZzK3Gy8NCOBhW43t6AlEfMR+k9fkodk9qVbaLaGj32RVDIhXMZGR9SexIZoKiGNnsa0W59wE9AxsDjl1YnE6RDu2xi2cZnkN/deexrXP8m/okeyrKZ9wFpXdia87HsbNDvu+ZX9h6Q74fwbr6Gy/bW8Irn3iSuIMj7FKhY33zt1msI/z/xVwaw0zkQnsXiMLW91UddwJQBjLv9nNm/bwoWxKIo/+eDLsEcuAj6kuHvQxaYntzJ5wiR+8JX/YM/+vXxn9X+y7j9+Yv4e3V/Fzo9fQ6JLv1fVaSdQ/77s7RuVSfvuu+/mueeeI5FIcNNNN7FkyZKc15Sae7lUeGWi27H210RPnQayzK4br8EvS7YgEB9R6pMBUVa4uUCPl+hxgBbRRb08iWbxEnt/P44XtTau52webOsgQhtXcCqT5Sq0mI9450koso+Tl8KmtRoTPxDk9uURPp+MjDbSfl4Zutzkb6Z71SSee0mFOrhULMUvS7SsbOC1N86mvukQf/rrMUK/kPh5/HQ+ENKrSXT0NBDqSBAdUEBk9yCY7nFF5uSlgk33CaYu04uTGMe/nH151wOH+NalRzkgukkguGP9IcBXMPd4/bqtZiBjTUUl46InmpnX3qKfyXJVVl6Tl68lsrcS0KPyQwePupYMdSJTnztf6rKNDcmFxrze5f0in/EYTwhUOUTCklVFs3gfCh2IlhAak5evxZ+MXelcfxOJmXpcQTtX49cOwcxJnoLC3Og0i0teiibwD+HedAbMAlA3CZEjEA1n8Jgs4Q9V2Ej8oQoIBTzcS5l/f3nPLhaccQ4As+tmcfjtt0ioKn6fxfsgYQ4gtzoEToz4pP3000/z6quv8stf/pIjR45w7bXXepq0yxhZnPfp63jp/ib6q0/h8PqPM1Y+QLc42fz9MnG9vYBBFhwv0eMJIfgtr3CrGM+4wGu8S4TxqdMyTiwGrr5BZsMxQXhclAu6JLYDDbIlMtoCw1a9r1fx4x8E+dQ1ffzFv4Gx8gFYD2cnPoMvdsw8x/ygtJuoqMLqtt/ff1Ze7TL0u/6jsu3413uW2ul+tlFmT/AA9/yhi5vfM4mfbZRpvDEvUZ5xy8O7+e7ieRwUPSQQXMnsnNd0rW0kfGMzUjTu/exsAZCpwEWh0LW2kQMzv4gk2dskRWNIAUGV9Cbd2skZrk6hSjuQRidiPtv2SqCzG3XyWLruWWGXlVxZG9+HOruJTM7t3s5FN/nGtfi7juXkkwldaxtJzLRHXIc6u9GmVGUPRIuraIHUxBmPxYg/ZX8hiCdiJKacgFRZ4bw8Ky8rTp43h5ff6uCSU6fQue8fhMaOQaufhuEzSMTjdG5IBaKFOrtd+Vgx4pP2eeedxxln6AUaxo0bx8DAAKqq4vO5N9rASLiXTfdtEoHJPYg3s3dYNjotlkptWQzucWf7DBj6W/UNr4ZF064391a1AT2xhBUa6S+ymdzjRtk80O0yUu5xo81ubXSjDUx+C3XvGBuNWShD8vEh7Qwe4jmCiXH8i/8Qj4gjHNC60SS4QpudxgtJTwryoRWw4Vr4cEDmFzFBTAiiwBF1kq3k494Owf3/aCUgKVwfO5u/3HAbUX+MT33jCDUzZCQlwdqvpCK+r5ETbIwrXK6E+QcLCY4fIN5fYcp1baeUnnhk2Uo57fjXwDURgpbN+hUfl1jx8bcAHxu2vQVIaFrmxDtOeKEzaH7zL2fSqR1jKWexkRfZ7T/IvMAks11uvCYvv4fI3nFMWb5Wt/+0/rS+dEMx3JvZ6KYsX8vcDZsJzbQ/0Fs+tZjarz9DcOpA2r3pBrd7ePeNjpdpR775TM+M4NQBoh6ejcGpA0Ry9IGaZaVqo3OxmREQaIMs4Z84JmsgmjMWJABcOe4knnzs/6h813n0Pf8sV04+mfD+o6Adza6YS1yJgQ/Mv4Rv3fsDPrt0KaoQfHHpSnsgGoKTP/RjEp09pu70rs8uTvOa7XwY8Mtf/pIdO3bwve99z/X3aDTKzp07ef1996IdixQswnA4ohVHUmax6O8slwju57TdVtmlZrNt4jVOopIz5JN5UexH871Mra+bTfETmCkfpUWdyXlM53R5Suoa/8ssSkZ2PcYTLAseNHN0PxyHgBTg7Ph7CY4J22RaZf1dHOAt+mkYo+/mqv0xnvP9ho8oqT30h2Iy56of9NTOVHKSMK/ccImtYMg9nxe893lMHTfGp9IgLh20zeY0NbuW5pzT1My9t47jsu7zTBqjUlq8a4Lnfpq99hHzfLxTlhdbDAddqcssdf0NOqkqyGVFED2eiy6B4In33m1O2r6wwvsP38vOnTuZN28ewWD63vyoBaI1Nzfz61//mnXr1o2WCmWU4RmXUcdm9vCm6EYlQR1HOT/gY67vbTZEp3CjbI8y3SF2Uc1udggf58qncaLvsC1hSEASyPG5+OX0W9CQlck1/LY6IVX203GOeShwJjUZL3nYs8yCTKU5f758KtOVnewQleColObLXS8hL1lllJEP/H4/l59/0WirkRWjMmk/+eST3HPPPdx///1UVeVKElvGaGBOUzOhWktpzq4J+CfZ919lxVuaqtM2bgH0Iz0tK4q3rnY27KaLg/RS79vLXjGOnULiSiF4JBFgkbjYdlY6IRJogRauDEj8ouJl5v3+ZZ75KvS8gHm06g0pxIflueY1Vnv/6iNn0dnZyx33vc137paIfnIn82v0ifn1r5zFp74S5TdfgA/HcZXv5OeEUYO5vmmr6SJXIz6ip2v0vJRKanJUm5J27UMzf8Vy7YqMfWjI/fG1M9JW0v6wghjfxfMfVrkyLvPLypc5/eGXCUgBpIDK7n9ZQrxzouuq2cr/B7/q5gsfn4AyvdvWBiOI70/i74OukV5GGcWOEZ+0e3p6uPvuu3nggQcYP3587gsKCCNVZKg2mrV8pIFC0DlLNo4mjPZbIaLueu9pXIR1oyZUqRLps9PWb2imos5bMBrox6BiB8Oue2T5QlJUva6dBooUJWKcBVVAi6dsbuSQttE44OW89unyFA7QyrWKSpV8lB4BP4ppzFDTV8vb5KdY5o8DMlfH9Sxj1qNVj6h+Phi/2iZyz8pFaHEZSRPMRma2XMfeT6t8VNbQvgJvhPUXpGifj72ffjeaaGUTryDH6wnIoKEhIVCkHiL42NO40NY2Iz+5pAjq1z9GuE6lpXGxnrs8OS7OE4JH1D9wg6LySCLAYuUyiKTc8L8Vj3Neh+Ch2PNcKy90tVNrYwM7xC7bSlpCpbVxMb5wkE39j7EseNDMK37/v/lYPKBX13Lawg3PN17Ms+Ipdr52Nqfd8RR7v7aASFuV2dYdYhc1Sgs7Yn4W4B6QZ83VnimNqTFuDGgZxo6VNheNtW72cMCuR2HlOO2R7X6yQpGiabYtw4r8n4UjPmk/+uijHDlyhM997nPmd9/97neZOnVq1usKEeAxe42eKjLaMY7gjNwRi4Wga2lsINo+tiiCXYz2p+nXkf6AnBFsTmVEU0CeKiMO2M8iKy73bLazuPXrmhFRmd3LL3eVmUt/K2av2Yb4bjd0qsi1MqJd163ihzKt/9/lRNv1Vdr04DYUqZu4GEcgdMz14bkvmjqLm03mib6DNvfxVFlwAdOIa046iys8mWXMerRqxk2CMX/sM/f5jTSm+757DhP27eBgdAFxbWyyAlgvA58VaHE/kfaxBELHCK+GM6rhZ9fBhyItQAv7og1MUZ5BEhKBUGosGm0zUnE+u3gbZ1XHUP9/9s49vqrqzPvfvfc5+5wcCBACJAhJSLiFqzdAxAsEImqtTnW8olYrrVXb6diZ6bwz06m92L7T2s6nl/FSW7EddazWVlRUQIKoLSoCgnIJN4EQgkkgEEhycs4+e6/1/rHP3mfvc3Yu3BTe8fl8oiHnOWutvdba61nrt57n93RmXn3vvHj5in4sTrXQkBrCVI+7S0IYDA03cXlYZb9sIpEyiKp6Tp95UQYnDr087w2XBjT7miCyw6JyaYaz3OmL3T+Y5o4hwF/ETlbTQB/C3MLZ/HbbJtrn5fG5wqVcEdVRIxY7Dlf76k7GxxIKMCwuLWm6XxSh8LEyNROPndaJKIfcubInWU0q4Umm4kkYUhKpIay0syd5iU+nqzqz+6w7cel3sxzCHIdKx+FR7MujNFKDrraxJzkHQ+SiFEEOs9mOmV21y3mPHHHep54kJfrn9G1Xz9iTHI2eqikk2+OE+mbTHtnSW1eu3ugdT1nJ9k6E556+N8/4iRvtG264gRtuuOGov3ci4my92Zc+cWeLUyDmeNvdVYEnbS2vh/IMoCkKMh6Yp9crPcXibrt7NqmmGH0qE3Tu1LpFInoaSyeTVazC8tQpfM/ohOM4GYJKI0tzqEt7W2eLVey7Sz5sFZOinxu77YjvztnDMuYNrdr8ir9O53nqU9Vuf2yZX+0aBi2mU5NaS5U5B9LROGcB3pQO9cnqLjMh1SftE3DBsiS/XTqBmZWjSOzW3b4E2+AODb9EuwLDwi3IsIKSsPtyofoy30yvFleH4OfWy9zENTl95kUZnDj0tzqHMFvth5YX5mB8EG2yMZCBTdUFfSoTqFEzZ55epFawQTQynTKb3EUYrOqzjbP6X85HB/sz7rdLWX7nWuaJTN1PybcDHemyCXa0mI4ljS513HHyvCfSyGwG9iQvDdTprrwV6odUiS7o57x1pvvWW59XHD4AmTSoS14WqOMgKVqe3m30hLe+ntp/VJm55Ce3Nrp6bUl2/P4NRt0+i0jfXMa7bhy+j1rv2MqSJNs72fH7NxBtSV/be5LTghFNjViER7SdMEj7k4THe6tzImD0bPg7u86xAZmfttxRjaLFejSgALFfgFpyXE10M0Y5XuZb7sik9cyW6Igk8Vo7jjQb8vOnnowhk9hvhG4v/mo0E9oik144T6IoAkV6Ql90+47X1dM199/eZBSzO89nkbmIebpgkRliduh8hGqwMLGJb8lCQord11XiAhaZLzJPF7wS9rOMOTmrnQxdLoyffp66H5xHfNsAlLBw/52oy2dd/gZKW2pZG1KZGhrbZf92Nc9MIZBWrX3PHtpIxSM7+ejOC319/0TiVf4tlEpvSlI8ZbxJNbbRG6WlfCfkkVoqMPPWQG2/T69daWKy3sQaodGZl2COeT6vhhdyg0lO3zgZw6SIu2PonZNfUc9mqdjtkrv85KHDfPzj9FirkgFmK/khjyOd1mTH0cnsBdsCXdrJRrKvV7oR79xwxJ13Xh00d07l6OmwOrnNdlJUVabI8faJuQf4PPvd9s7NntYW571X1Bi1t8/o8n2Dnsty6j0Za6Mz1kHXeL0ty6vX/NxfOLD4PdRYrgd2tNQisacXZfVC71jLEvEkoj2J6mGLjY5IBnzTL59qyFdP4oR8RY0voBj7exeLaOTGIh6LzonW60lny/w5dG4rsN/CgPABLzzmxEh6ITIUSeWCGqIeo3y07d8yf47r/FOi1BDxZC9CUSEsiD0CaqnnzzlrjAoEJ0uADFtkb/us814TsQcf5CcNjXFPvJaJW02pxP9eIPbY8eVrWwQfbMq0zdpo1ztuK5zTodpBmQo2giDtpAtqmZ2AQxoh8u4zUUvtf0f/JfO88Xvgta0C04IjpmT7sCFEVJ2Je8bxfmgPnSnJpYymOBqj7ppFpJ5P0ucr8Ll5aobK1cOYWXvrXJINfYkMi1N+/0p37LbMt+NvnX///kELa7HCDYbKs7rg1oXkpM/MjIeKlLn9/+g/Ca5an3GEe+ls+OpPM2W88rQg/hu7a6rD9t+9oWTP8Dz3RlIuyvDzZJh5+t+AIn0xu29XvsDVWy3yFZUWy+IVS+GLuspTUjDAlOTfaQ9K52PQ50743E3Zz5GZP1Lim5PPnfsiH60s4JGXm7n780OovPwA3/9OZgL+bG4B88wWF+F48UyF5LYyZsbPRRoaStjeKJVGahjw6zYSPwaxB5TiEH/caXBdLKstBnaHOJnEhmbmhjOO8XvsMtz+Lw4hG01iD/vfE0cv9CvBk/PtZCjPhATTiucg9gygNFJDY2paDqzt9G3l4/4Nd8fdmbmpKN2/c0F960j26t/Te+m8E0rxiV8bnbHOXseOpqyjrfNULGvk9MPdhnydFkZ71/WPEI4d7NUuK6/C6lGvNzonWq87He9p4mTENXa1c81um7vTxaIksoyImnlx3GxDHp+S2MO5UHlQnLZXnJN1dn+IpEapbmc3stMT9keL6Qizk1JtmZ35ygNpl+UvRbeOgMzKQKbDLkXQ+m8KF1+QWdDf/Ksg8e/TOVet9T0XwB5ZjZHMhzDERll01OqBiENpZCkR9QjPpQRoYQ4bYymwBvriqavUTArCFaEN3PO7feRVHGHLHdWU3beKvBFtbLnDT+vqPKczRt4Txxqxhb1iA18NK5746SLm6he6ukrYvhIYu6CGWIUVeJpaq/2Z60XGKD2rCS4cNpdEXT6mEKy1bAThGUNwTQiS4IvTTgiD9eGF3KqrPGEIzk5d7d5pe+ejFUrxvnyJebrgPxKCr+tkxaaHuPHxPF5a1uaiDl5R1BiLX7fYXDeQghXFnF0/wve599SYPc8+vH027+x8k3m6xdOmijZgNP0PbaGVCZxrjfehGooq3b77oGAzfVpqOZSayBRlvKvjzLtUuC8ypaLlRRBmp4vwOHPZ5h4/j5TMnddOmx3E5DXjr9wSbgocy+yxV1TplpeNkjl1O2PeOxrZ3D7LRrpOxtrY1fvkyOkeG56t09V621s7cdmaP5yacdpHI+P/ZymaeviYJ+ax6Jxove50shfxY5Wu4PEgWDyobQ4Jiu2EFqwfe5ge0z52J9nwuCNb7qhG2WO3XYkIVEyiI5KUfnsF4v42lN0CkhmoM/YwiB/ZqQiz2zd5hMojPxJcNAMURUFKSW2twq0V7/lORW6bFtSQuB+i37HTHAYZPZkEBwctAEwrReH1G9j4zFk0aYcxZS4j2udHjQG1wX3u1ath6ohgWtfKBTUuUc3mW+wTeMVvl7L2G22Utis+yHlgrImzli50+83ZDKR7gMrHa1z+cIAnHxGkPoK290Xmnn2ySuVP7bH49T8JrloHoHJFCP6QJn7xeo/nRTU+ThSxWGmiOVVMXlRDUe1Tgxe2/OCSRQwvNVnyqMII3R/3HVUEnwub/OHXbdz1s1yDvXo1LPlgOONnns+MCwfTMGM/i95cyYjUtkADny2Tf/86a64YyZL4dobOt2j+Qy1tgNJnI9qAM7DqCnzXKptvmcuw+95m7b1HuCys8rRSi1BGEVJVxi6ocedd5cPL2P3j81BDMUq/vcLta2cuAxQSPK/d8U3rvn+pIN8KHktnPB1j3NXmJLtuOMbg9pxyTs7aeLyQ/MnQO5lldbXe9tZO9CSnhdGW0s7Yc6KpEk9EWSeizrELalxoyOvh6XUa8cLjXdGmdgePe+FZr3gpIV2qUUXaFKXe9icS7inbi830hsY0W4JoKMcuqHEhxMqHa1BLM+2P6/bisu37dh+5Tjk6dprFogQ4zIDp9l30EfzlXcnF5yu8tVJy4V8URAtde42kn83qtBm7ssVpmyEEBwy4UVf56QuCxKh67v/tQf557ghqjWYmYMc2257fcbf/UynBz/55IP+z7CCapgZmPPP2a+WCGv77oTauSsJCCW1C+BzbHF2nrc6/RafBq3+y+cNf7idQFIj+Efp9SfLqZiWTzetHme+EvTm108Qver9ylMPS9ZKuXFDDvB9O5aGta5ifdwGVC5a5c80Zp1eeFvR9DOSXIXGjROwgk5ZUCBIyXc/7uVCuaQp+/I+FXP/oeQxLc0kPKx/M9lfziSy0n+WKeXYbHRrW7D4DmF4wklUX1dGy3qB/hySEgtkhWWasZTbVtqd8aRtSwmutWwj/ooOrHUpYzeSlsxe61wbOvEv8GMZ8ryYHEg7CKHtaC4yJ5KRq9ZbjnXvZ74n3XfPVfRxZ1rKf4WSsjUHvk68Np+B15fGWdSxzw9HpSU4LePwzGtMTU1YQ4cbW+dWB1JEOFOyID4JOS+yx3FN3V7tJG4q0IAW1d8wl1TzYhu498Z/OCd8Hj8c7KYkso9Hww+Pe9nnbFnsYEj+G3durqZn9Ln/33TZ+/X3Bbe/n57TfkbrEXFKyLyCJ9rHo7AiOKy2NLOXZVCtXhzOQ75PJYuZyYaATkRbTCf/bSzzxmA71BZxrlrKptJbDH0eYa1S6yUW6Gk+HrtQQgudNe6PwtKFxduqqQCY1BQs1z+BdYzHzdItnNIGF5GZL41ldIBnFgM4d9LkTrri5r0tj+s6k57l6e8o1rr9I9mGIKOWSvlN6Pc+S7XHWhV+yoel0GwH3b17Y/dcpydQvq3zuhhB7mlI8+m+DiVi2b8CakiYSYZPZ98+gsKg/m/72IealUm6ZE373BtERRzCbB6EPbyL+Fdi9rZqK365i1/3nk9htz5FVynOEVMGNus31nhIq0+V1bpvXiM0U6BvZYMG3oplxc+7x1+TvpaV8L0N2D+acIyOQqGixCKP+61WiI46gYlE7fy7J3X0piSyjKXUeSTEALRbJ6bOxC2rYc/+5dO4egCmsnH4KGktv31pxI4cyuPaWajp3DWDck68RG2UiRdzn7BaUS703FMOn+9r4adR5osvqicb0BGeX/0xOZdk6v5oP5lzj+/kk2MlkErbeUUViWz7xu0HusUlAFExKIssYlbeQUXkLXUheUQQKJrrSlnO33pM4J+LSSA1VK4/wowcEF67sIbWeItx2lKZzIOfopDcXUcUP+RZo+928yUGS/MElDN5ZSmWqmJFKIeV7KyiL9gnMBpYtB61BPJW00FWVvkgWpyRqahyv+wK9MjI8soK3zMVcGbJP7lekIJFeoz+XgNbDh2i/VM9x/ppWeyWLUnaoySIzzAgxgjJ9C+8ZG3tsoyMr1JVuvVeGUqxQVxJSQyipSpakJHuEgq6qLDTh78MK+xaEWFt1FcXWAIoOFFOxt4KRSiHjGgoYfnYxRSWF/OU7C7nSSLpl/jeL3Tm8ZX41iW39QNrPnfp6m++qZKfUuCJtC68IwS6ZgS+9seRFKrSYthu8TQk7iD9dvJPC/xpD1X/dwIBfTeKFWZvQ006ZTv0HPleAqLMzq+lqWzqOOXiebp1kaSu3AAAgAElEQVRfzaCP16Irbb4+6YrGtjdSElBf/JvQ/rcg6o+pyM/kNJHTxmifCuQkp0qdn0b7RSIAgkuCTPh/zK2JnL/F74bhTTWYX29zDepw+bLPUHulNFLDqLyFDJcvZwy550Sezc4U2DagHJUL3lAoR+1Sx6nPqUckEihYKJi+H2fzkEhD1UA6VnuAry1esRIpxj5Sw2VlA2jgMK+JbdRbR7jwSGWOXra8Lj6kQ+RRqCrUpARH0NltjGYCo3mHehIiF7Lbm6wimobRwd5U9E1jqvmqSn/tEDd+Ic8dJ0XY/Tjh0dfR88tYkpKQqkQLb+OysIKQGzED6gl6zmzSFCfl6BR1PHVGJSXWRP4jYVGIRFftOOo31L8CkksKh9KgtfKa3MYe2cqIz9vejYUf1ruw/SoLZupx1ohNKJhoyRY67pGIPbA3MQtD+DdBYzR/P4zWMpD8G54NxtUhyW9Mu48WmWH69qng3Dsm+SD6ybdNZ2Xfdt841SerMWQ/6pPVJEUBexL2v7t6lxx9t0/MSs710Nh217eOSAkiqWJ1atR1ZsqzOg2khNjPoe+f7bBMKXN/HD3vT3f19bZdx6t3utf5SZd1Wtxpw6lBTnKq1HmsZUldQU2ZvrdV6iHUkTGsnXEUQ3Zlf1Cj0Rx4Of61AL28XL3eltedTml0OXuS1TmLc09lTTXVXtfn6JXK5V1+fmMY/iMp+W4evGLCHZEWdDV37yt1BW1kjNp7LmNT6mOaaM+kmSRz9w2547RGbGaoupm3RSHf1OAZE+r1MhrMOLvl+9zC2TzF+3QIgzmMYmI6s5hE45Asok3sc+H79vRY2xuMQhZ+ZTJzI+vIyzOxDtmx77V3X8aZKcnrVhhL3e8nR5EruUTORIYVH/qvGNImX0lJtGiaNKWLJCZV0TN5IfUGZ2kKuyy7PY4T1u4fzmVdfZJGK87N+tk8Yr1D4utr2DAqipnKp009RAQ4INPXA3IrhlqJMqoYuS+MTCnISAgZCSNNxY0dP2gNzmpPpr8LsjYYpSruybdpnMG55YN8YzmsYjAflG1g+s7g925vsip9PdL7d/OSWC+vHtLlbZlf7XMyFUkNqSts+Uo1sQrTjv6QIWTWFY2qmCjplzonYkOG7LC2gPqyRer+8dcq7DWjx/ZXxBBbOrpcV7qr82TqeXWyn82n14vnPJq+6E1ZPclpY7Q/k+MX88Eiyn/2LJH6FsBO+LH9wfkkyscgrUOEb6tH3d3zS9GjZF8Jp+Ohj1dKI907tHwS8pYF00IKDyYsxmlKoMEGu69lLII12OTjxz/ii++ej1qX4namBOo70iBaWaM0EhXDuYlx/DxRS5wk4QkWA9oKOOujApcR7EP2uQbbkTn6TBal/mTzh0d0IqYNL79iQpU4kx+HNjF7mAYHMydo88EitB+1MLtuMmu1P/tPzNEDiMFhrG8XIssybE2hOxuxvl2I9qMWtP0OocxL6XojzBwyC+rtQW//WX+GfrORy8MqT0pBu2khNYUDZ5ZR9rN1mNbljOUc1jy5liue2Mf+eSM557ppmMaFLLr5Ycx2g6vTVV8ZTvHfk9dyyTeuY+fgLzLy757E/NZgdpbeSujOZpQ6+7l87clKqtIybhht2+tcg35EnkF7qj+z1XGIun007DrAMI/hbti5n2F1XeeOHh5ZQZMxzT35ngzxRheAfRfd9q9jmVT5NH3CnUgRZ4N5C50U+r43TnuKmGK/79l32hvMWzBus1B294ymmA8W+cZf7lcxB/ccGy73q/BtA7Wud6fRT0Oyn80rvXnOo+mL3pTVk5wmjmiPIg4dRiR6DolSo7JHvd7oePUU3QogEclItLydxC4/x60wNJ+h6rnO9GdZSeiDG9YLHZ9eGlbOeo6xj9fwTsX/EFdKsaRO0bwH0XftD4zTtinCsiZcgL+WWqYS/T/CF7/dMT83NCuwvGzpjc4nWJbXIexJQ3BDCF604LqwmnaecxZtxb6T19rZxBsoisFBeSbTQqNQkEgDpPQwvKkKikgh0ajhTfKVTqLWZM5Uh7FBNmCqGzD7GAxasoZNM24ihIqJ4FJGE1YzJycFC11r56+yiYHqB7QaE+kf3sZBK8GHcgiF6EyW49ig1JJQDC4RYxikFtvjmGYHe4bF/H0k7nO0u4SLvR2U2zHpeeY4dx2WEzlHjEMJW8iUxjNyoY+Y5VdJyaj+Cl98IUMSYxiC38+1SUeeNlTOUa9EM8OsVmpB2cA8D5nMHwyVs8vupHzf00Q8vgSOA6Mja6ilILSBVmOSD4q20jHntmOfynmiCiEL3Gf708U70xD5IBp27mft7zZy7VvloKpsMZt8lLV2j5gZxr6jfjd7oycY9+RrOUY72dCHyt/VkDeig2MlV9lyxxyfX0vQWhYkR6MX39o/a9OevWicjD47EWuoPCn2pCedqw8+cfrHaY/7n6WIJhN9eFOgV6RXTsU4bZHUSDXlprZ0xEkpCCcHHh+7YLnt8apbOf1X1XkVNMdgcJxaOZcE/QOzBAXBy07Mto/KcX8Uhp14ePxUKOuZFO6J7wsh+M+k5OyQfedcHbXTjzoQfkmkBi0vxdttSW4MqzxjrKfywfVEK1Ti99je7UkxAAVBNM9ksLmCiNrGOkNwbQh+2dlKs2jDVCxuC7XyQlLj/V/9lDqlib+5upHnnx/CATbyVbWaovAqGo3zKdbfJa+PCa0zWGDC9/tsAuC5lMI5lkFYjGWkWkhclmPptUzX3sk8nAY1KcFZQvKCqXGrbt/x3hqOc9A8QrH+Lk2paSTFAHK85NPzbIo6ng8u38qX/2UTm28ZRvkP3mX3fdMYucdPgTpCE5xxq5/V7bF/kXxBNbFheZMFxvOcF1IoB95IyUzImxAYUjC+9QmE0v14nhc7k2XtKWZn3R2X561gr2VD4gPRGF3wDrsOXega/GvfqmDNuq1sK1/OkN2D+WZyF03KIJL6QBaam/iWGETIg7CURJa7fPYnA+p1PNa94qS7haNZp3LzAlQ+vjxLJ4bVmUQJ22uFN+rDZ3j322tGj7I/hhycdL/qXesc+bTh8a5k7ILlhGJ6l+u2I0Fpi49Vz2we1O3ncJoY7S03X0LoyEFG/GpFIL2dV+R+YHD35fVG50TqbZ1fBftjJDq0k5I2L0iclIPeNI1B9IDxe4D9IDrtcM+Mk1cvdrRG2tHMQ+Wo5kH0P8EsFry33v5b8gh4/ZnOVSGaW9opL17P8QgwXFO4LGyHFBlCoKuqD8J/osNOrgG2B/OCr8Jt6Y1zaaSGPUnb4OZFLER6MU1I+EsKjij7GKm2sFMU8qih8A8FUfSaP5GItvPHRQq/6dPCU4agOPQauqqiKAJnzLapr3G+CjUpSXVY5bqwitAO82CilUbZhonFN2KddvxVWgwh7HvjiMYDCcmSFISUSejqTlenNFLjO81m+LIzv994fQw44vvObstvdLeGJHfd5CesCH/oJ5AZoIe498YfwOH9/GzRz1hoqnxRt2H+m3rOqeBKV3m1q8Mqv42X8cXoISAXHr7G2ERk+xHqEuNYYw1hKduJdoa4nsk8z0YSwuRSRvcqCuB4ZdvdVR6e/ZMv2+6ucgl7ts6vYsS/r0I80OYjJlLz7DWjJ1HzYNehOV0m6DmVZev86k8l5Kvyb7ov57Qw2qWRZZihdoyvxTF6IPY3I2FCXeSIPhqdE6l3BiswI30JRdvTqRKDXnTbkJ8oD0Mn5aA3fZ7xNQL7z4yECUkNieKm4FOyskEEeV/H78mtV8gE8XvhnRTcO/cfof9guMSjcHg/v3jlPzm/G2/u7uo8Vr0TUZbjOZ6vqjyVklwXtsfMYRG7VffHakdEypfAIqqI9HbIztw1XF8BQGdnf3QZR6JyQ0jwZyvEd2Ot/DQB08IHCQGaYdIp+/F0qp1r00brb0LwVApu1zW3rMMd+a7TlnczscZSOMQ+/jV6kB8nBvKekWCa59m8KMLduuT/dka5IzQGqdUhUZBoiPQcccRNN5lQKUn/LgwNK6EhhYIwNKSEr4cUXjDhVh1eMOF7v8nt2wMjJG07JfmqQpuQvD10LErBUCSwVSgksFicUhikQEgNkbDCLoV8rtibF5tMRMnRkmhIYK46FslqOjtzHbgcHYAiNZ8S0Y9C2YeRaiEdwqCFeKDBDn43nZJ6ToEZ9AyVPTAa9obQqDd6IqlifBxm9MMr0m2055XxNVAj2DtVR0oSkL7yEoYKIngkDCuc7tuuDwGZcepN+0+M3qldVvdyWhjt2OMgmqKEhrT3qGs296zXG50TrWc2R/noX6eT2t0vh+TkZMDj3jSNTn1B8LgN3dvt1+ZD/W47BV9pZKnvvjAQHn+sa3g87z3gw8EoBUN933HUTkd4/MYw6TttiCr+k6GuKHyU+FtXV8GiVXuLNtmcYb+aBv1+BjIpqL1jrksEosV0holFNKWmURReRcgcwhJlByXWmdRZBnPUiTRETAzy0ZUns+oVfJS4xq1zufYW85zTfRj+oMFtAqaFFYrFFGIPr+KBEa0uvPnUY5JbvqzQcpk//nxy2ESLhKmP2/MhKFWlm/Y0L6PnPr+SSfmpYNE28XkWbxF0TpP0G6P5iEBIwfR58Oz/jfBlDJ5V81j/+XtRAWmaRFWVL+vw3U7Jdx/tS79RHbA/Rfy7BNLSjnvyNSLDOkg1DeKjfz0rB47dm6yicsEy1Pth7+4qon1MUtJPVdX3YQvxALDNbv9MylmqbKdRtGEiuIwMZW19srpb7/GxC5ZTd/80Xzt6eoedKy2bRCaYA8Dt6x7S4fZWT40I8ipSSJHZsOc/bv8/O5sZ+6MeeLyb+/T9KQoG13SL251IeLm3eqdyWT3JaWG0a2++7P8rRjRnMfukxKnvaBjRjkbi38w4moXKQf/WMRcFgEijAabMRyOO4vmbI3Ys9fHL+ynYjuT6sOKWl12XU9+LFgxSYElK8LFUfJDvYeH32h0eWcGt0RAvtdtG/pUU3PolDSth32mLutyT3fCIfcI5Tx3FEiPEbXkNNBnTKNJXENJ19iSrqRf46q0XcJ6nzgFWS8b4KirRhEDoGgoWwyMrML5uX09qebDkiCQC/OGZMOutPhyihYKQlhOydTTindtbRDP7aOS2vGbu+kjjfqHynfUK5nZI/ATkHos+j0Hn/RrGRwovTZrFGRuW8uL0q1BD9tJ09ptPgLQAjX+KSJ68u43bImq38KwwNLbc0fW8dslYEvbvIV2nIX6Wz/u7/R6NiJrRX9ZZQZPSwS3yzJywvZ68x4/lfXe+o8V0Rv3Xq8TGHOrRn+dYRCQ1X7y22ZxPaEhuesjsbGYimo+a6DmNpIjm09B6Ubee9Z/2enyqlfX/BTwOp24w/KdR57GWte3uKpe/3BFpaKAEl+enN07Y0G5YQzEttxgpIfaQt7w0v+7HdClSqnR2RglL/0uvYIEC9Yn0HVhSoTS8BF09kvkbtjGYm7fOpTE9VnjcFIJfGQO5NtTCEiPK5X1TyCEG9dvmYAj/Hdx27SXyFdtKtAF/pys8n5LcqMOrOswtPhfL4yFfn6yGlIJkI0tSm8CczLavj7F53T2c8mCPU73wL+yz1cnUJ/1lQQrdGsQL5gEXao5Yg9xQpvpkNS28SZuWiU9usYr5qHMmpZGlrrNUaf5S1EHtbG2RtDKYhNS5iXH8Z3ILESNJIQepkhcc15zdJJpYyEauC+1nYedkpqjVzFMVPuqUcAe8rq7jNv0AfWQre5JzaBTtNE2axUOqRsu5l7vljPh4O4PSBDPVYZVI+orBStiLX9DGbdvdVciU2uW83pusoiSyzN+3WUE09clqSiNL3d8rVahUbZg3O2zPi0KcjLUl6E7bl+r1OODxrXfP8iEA3jSr3UlkQO/1jNbuvcz/t67Hx6pz2hjtU4Gc5FSp85jJVYzge3ctL7c8icaexKUYsh8lkRpCMZ2G1rMwOvtRGllKWO2g9pbqnB20FtMZ8+gioLXLdu1PnU2DPh4rixy/JFJDUzJzYtGiYfbEL6UkUoOzPJtCsJBNzBbCPQkFQdrZp2URGYSaOICCRZ2p8KQxkDA6NzKODVYtjSTJP1TJGalyLOFvlylMWjWTeWmns2tCEI3FGHikgyUpyaAr8wi/p2V5AdjtnxKfwOumxWx1HDJd7OviQ5+DVG/HHCAS1yhAsiQlGIjkiOp/zupoVSZO2xOf7HWA3NN2Kcs63uT2aCOvGmmvcqWQdlHOLj5koJxASA312K4toplKdYhPr0m0sZit1HOY65lMrbWVRqWRoeIMSmNDsOIGa8RmykLbWRifyJRbbIcHKTrg8H4OTLgIDtk7PmmaFCsKl4UVnjEELabFQWsoOzpnulcKXn58R5x5HjSvIT23k5d12/9bRDMkL80Zg09nbZGIRAgpMpzitbfOpezfVxMbcwg1lIG9HW/vYO7xXHhcGhoymTEDqaZCZLLndh2Nnjvxu5D/retxVzo9yWljtD+TT1a8p4f6ZDWapmNJw2YPUp3TcA9kEof3595lHd6PKYNZf4LuTZ2/N4k2lvI+EWwP3oeMbSSNCF/U91Me9J2E32NVU+z7/dLIUhRzOkVyPwOJuSFQZ6gf8qHcwRkBpa1QV7osYY7T2ZeAsdalLLHqmP3HyXR31vEa6DViM2X6FtYYIaao47v5li3ZBr5Qa+HzusZzKcF1usYfjBa8u4WQGmKvNoxHOuto04fTltrMbCbn9G1/xT6NX6fbXuUNog0TSV9ZHEiv6Rho5/dRDGIhdviTs22wx2g7Esk8zmItDRySISpoZbP6AaVc4uP9flpuwUyNIaSGGM8Q/vn591CiOjJ9it6hbuTaiHD7/TsJ+KrMkKR0JU5msmMVZ2OYHdp1ssTbt13J1vnV/ustqbB1fjXjnlxKbJTt/S6S9rVA2Xfe69KHxStO7u7P5PSS08Zon6pwxqdR5/GXJXuh59cRiQRS10j9thjxf6PIbcEeoQ4Ed9458Cj/CUD9T87FaIxRz2FK6M94hhzFc9p1FKl9bQ9eHA/eERhaLWUhP6+4kLYHd8YbWLrt94KpMylnMVtplkcwsfi6foRnzXhgu7J5taOKcOvsKqQo0/6MdGWwuuoLn4FPTADgoLDpQq8L59KFAjw7ZQtnfnEWz3zlFaZeVYD48zrWWCGmuIbY7gPnbrzWUmhhH7rSwn4KuUhOcI2I0y6vEWuijZepZTgDMuFP7ZnwJ+8YtYsk69jL3XkdPGsmsRIp3wboylCKJ62VXMJMQqrGZIaCqUKarjOprXT7fZUFl4YV1hvbmMI4u//DwWNeuaCGXfdNx/g4Hz+Anpmzdoiavd1w5oaz6XA2hs+zwRfalT2HgqQ3OqAE9G2hZ4OQaae3vK3z52SVk/Ewl5J0et98V2/sguXExhxymyPTfSYNFSlso2/nuc7m8e/ZkB+9Xte6n63HR6dz2hjtUxXO+DTqPN6yHM9U6C4152s5qTm3/+QaUqVhFMViRHQpeQ9pKGW+r7neroqqctF0+2/tAz9kR9NsXpFbuZSxhFS11895hvVq+k67mplUsJRtrgfvnSH70tcHj6cp35wloiSyHF09gohm4PGS6HJe6TiLXTRwn97CQ0Yhf0nBITEILZbbroOWn1e7UyqYkcHQGbwQrc7fR0NZJ6WN+ZxzIGNUuzJYQX2RbeBleCIhNURVe9f0nMvydtD6URtrv7eWeeJM/vrcFvbLIsbzIUXhvRwyp7voSB9ZzCtmIzfqCjvlQTos+EbkIP/duZX3gW+JQRzSkyxO1BIhxBxG8TDvECXESAopZ6Ab/nQolKRI5LNFNDOTChazjQZxGLAIc8jXtwOTAYlFrIyHtTMXTWFSp0jahPRxjz8jN1AS2kwoNgQtkU7Nqoj0mKcNnaEx9rHlmM2DML53ALEH6tMJPUoiy4moh3wmRM2LYXYmGQk0JPqgWRPTCEwCU93AhdGdCDR3DtUnqinS30NPvx++DUBaJ1scHacd+0Nxt2+vZzKLlPdIyk4ukeMoVIcGztug2aarNiFKx5filDS+Bp5rZvF18Ma0ZPIC2P01jBqffk/tz+6znq6knPIaWs/q3hHtf/l6nK3Tk5w2RvszOXGS7c2q9cxRD8DIbzzFR7+6hfQaydZ7qkiR74PZnDtth3Jxd73gVzvKCMs6HzHF5eY4BmevGAGyN1kFSCQatTT5Em+8b1lcEMrVN0S+u4g6yRw0JeLC443GdCrV/owWJSwVL3FfrJWnjbQBDBA/j3WI6eaVNBoSQ+ZmtHcoMM8sH0TDrgP86fENXPtWBZB7YncMVpBkG/injDe5NDonk9pR2ZiT2rFjpMKI0cMY8oLFSKWQNlHObg6SUDTuS1pci52KEuDSaBUvJJ9hSUqwV0iu1uAXicEkCXM94+xxMkz6oVNBIZPVoewWhxjLYCZRxGLP5ulKfRKpuMFCNnEFleynnRuZzALlTeaFD7NDZvo2ewPkIAUZb+nM898ZErxiKhgyE0N+RQgeTw7mMq26yzttxxFNy8vNbe04mGV/b2+yiqQYgCk20kgrjaINoVh8XT8MqNQn5mApgxGdnUhUT5IQxefk51zDgD8bXUmkJp0T3jZexaH+lOBHjlqIU6gOddsJ9rWO6OzsOkWtkY7cOAYyaueZvadgb/sdCaQ17qK87IQ+UdUKfE8+k2OXXhntNWvWsGHDBhRF4cwzz+Tss88+2e3yiaJbhIpaEL3wVgwXtfWo1xudE63XnY7Xm/hEQSiKbhEO6LOg3XqktJXErr5pvvT0SVUH38a5KAEfw8hvPWUnANGh8pEatDI/f3GktNWFNwFGlKhMyD9I6OCZjFQyxBSDzRiB8LqnjVYilc4bfIQ9yWomqMVumM3tTKE0vBTo9MHjpfk1kIK6zrkkZX9K82to7JiOkRiQU9eIvDfZY1ksSYEqxxHOg0hpMy/X1jFbOdPVyxjKDQxRBGP7vIgiVfZEqkgaBShhgUyprO67z+WsBuz/f2kiq9dvZ1qimENmIW2iKcdgOeOp6JbrXT5Q9Rv4AVqTO55TGcPyVJI5eaORhnTHbFhdHgP/oYyti9bTYBxGILmK8WziY1LKBoqUjBevYkhKrIms50MmqTBS1ygXSaJWZYZARMaZyQiWYscnR9Fo4DDr2MdhOplKP9bRxE/i+yhjMNczmbU0oKJymCTnyWEc5JC7ubASqcBEHq+Tubd3+mKgdoDCkMaglKCZ3BzmWrIFQsGWqnJBDbt/OBVphLF29+3y3lZIFQX7qmO4/jo7O+fQoGwmwUAq1BY+EoWsthSmqZn5WBqpodGYbtO6GlPTRli6BtpKpFDSDGslnpSv4DXiNjyejRxdToX7uVNGKNlKUeTdLg1m/F5QRyQQTXhRdVuycwN4CFEgzcrXNgfD8sSPJ9sQWZvp7OeA4GiMwIQ+UqVOqe6WEe0zePzodHo02r/85S9ZuXIl5557LgA//OEPmTt3Ll/96ld70cwTI9LQSB0+cV6NJ8ND0qs3dkFNDplCb8s6UXCMNLScOsc9udSXdAD8vOhekhdLifDuoy8TLxvJtC9dSb+mfZCM89GvbmH4TxcTrm9Nx8/6+YuDeJDP63ceL7Y08bHMEFN09Qzmb4rRftSCWpdCi+aSdgSJFx4Xroe8gvmbYnaUfZnQnY2E9ytYccPnRV2frKYcx9lrHGMermH58iRlOzpYY4R9jmJT1PG8bpjMVieT1GxvetkaAqm43soNZQnODEjr+HrB20ytG8YscSGvxP7MjRY+aNvpC693f/aJNEnYHc+xC2qI3D+NxG7/6zu17Qwe+fk7dCitlNPIXobzJGsZQoxipR1dafN55k+Jj6fVSnJY3YV95O+ggSM0iXZMBFfkTaTReIlms5Cx9GEddej05e+4gCdZz8fKNn4Rk9wf78c4OTiHLawIp8/GuXUSl+xLDWeJshs1NY71bPM55q1QP6RKTHKfvzqs8h+dlj8m3ipkaWg3t3VxTyrSHtGp5u49l+uT1RTpqwlF7MiI15R13B2BfLWVNqHwfOogxdZlGNq7bvulZc8fmzEthCTk/huU9JXOMpqMaenPM3bTO/e0aJhN7XbK1ls5k2eVv9Aa/pAi61IM2T8N4x8JhKG9EvuFDXszOJ4TS41h5wdQS9P/9hGi2KLM15CeeWRFCpGe97IkUoMesGE4GrIieqA7/Qwe9+v0JD0a7VWrVvHMM8+gpne6pmlyyy23fKJGW41YhEe0+XLBdiXREcke9Xqjczx62782CwA1avp0Omp1kOmsYenT6CfhwenWp+aeTLbdXYWixex8vB4q1tW//hNmWa5H68h/egpSdmKMCrGKPHrmQ96casnJJz2Z0kDd8J2NR/FkueKFx31lxXT3c5nlfuyc8jZ/dSaNykvclOUolq23N1lFNGTmwH7D6vJy0jrWbWvi410HMRGE1BAyPpEl6qYcaDtbsiH5i/VM7LIbhxwg52yKgtJERUjhWauT+6IN/HNiIBeJ8zECaDer1bNZk4qwRNnIPquMw55x2mw2MU58jpsxWRd+icd0i6eNJKRguNrBrRELUPmGfojHk62BbGHZjnqmEGymgz7GGGYxmnXhl9x7+3dTglJqWSM0qsg8f5FUecGUTFBgo4SIGMJQalluRrlcx70KyThs+aH2bPFSlHojI4IcDpHkbPS8/w8Sr45t9HJpgQEXOSqJ1PAd9QgQot5S3DYKtAzBUFC627BNbBQqB3NX+u9ZSaHi38z87tWriUTYPnQgha0HOJuuT8HOM2T7AXgl6C7bEVNmOwN+JscrPRptIYRrsAFCoRBKd3kqT4KMXbCM1J5O8kYd7HH8pRFC0bvPD9udjv/RclPZBUvXelLa3pqJugHs+t5UknX9qFxQQ2RYO1IobJlfTbIucyI/GXDMmEdWEC07ghrJXTjGPPI6iboBREoO+9qiJpMgBKgqyAwcFvu5zWZVsqcGPcBuBBE4XPfHdUy8dS6JXSGXmMgEIKgAACAASURBVOJonjOTmKKLk5UXHk9DdHsSc0jKAhTsRBpbO3YzSilL3y3a948OXCnRUBC8N+FFrt5qGyKvo5gjjn5JpIaI7GCPUkXSk9JxatsZ/OnxDXDHJEIhhaX/5y/QYnETZ9p3xNLgrgeaWPyDEcw0PakiPX3hPKsNyY9libKJgWhUKK9Sl5yLRV/XOzqxy7/YmsKkXvmQr4dto3PQaGG5pTJdDufzeRt5ItHALGUaIH1evVOjY1meTHJNdBIyZW8ib2cKCBumDXKg8xq4rVKlTtnLV2VVDluYI02ijaXtGc/stTTwKCu5EwPQuDKU4kGxmVvTBpzUGPfuvlnpw3jRxq+sAfyt1sJGbQtfCiv8t5nkVb0fE49MD4Rfu5pjjiFyvM0dPR+6IQWJKBD3GOguyrM3AcHXW853vfVl63VHEesQDBU8fARtRObzjvkQ/Q7pv/WOXMXRu/+xUYyfeQEXlw+mYdd+/vT4RtfvIqjP3M2Hh9Y46SFHsvMpdOFolswlrsmWz+Dxo9Pp0WhPmDCBu+66ixkzZgDw9ttvM2nSpF408cRJ7S2fDI1pNs3nieCUdSBnb521t14aqAsnB44JojF1Yji3zK8m1TQ4pzzf5kXJwGHxr9l/qvectL2EDkEEDlturSax6+jT8WnRMCLeSU9eNoFQnaKgpOF7UwieYxf/LM/otix9I+TrvXMUU6NRSCg5xV37VgWr122noayT/AMhxnUW++6ISwpauax4NJ1tkuUD1tM6aZdnEyyREgZsKGfOobOZqlay3DC4Pa8JNWqhKTpKh0XtrXOx7z1tA68gkKi8oa6kQpWuMf28rvG9TsEt6mheN7dSEj7MGqMP1+TVk9fHYnfrDNsRyYDbo000pUZgyHwf1CviZqADndfAjdMkY0ydkJLZlGVLkZpPiVpAoRl1+wNlH+N1zS23XBWA4tswvWDEOaAcYYvsbxPhiFoOkKTOPMA1Gjxa1ocPB7Rw7Vu5RrurOfa6WMfs5By8ULUVN/zoRirEefErMdRwj+V50ZuudLIN89G8w/XxavoklhEVbf64awOkwCVXyeEH1/HpK2qUV163GD/zAoaV22kJh5UPtv0u1u1gatsZ3T6jt/BoH4tEhzeXu0lQBsPPonmOvqyepEej/e1vf5vFixfzwQcfAHDVVVdx+eWX9/Ct01NyvapPLCfupy3e53MNeBfQ/DnfuJkPfvs74mUjkWgYMh/Vk8txeGQFoZSdCWjr/Ayhg9mcjz487lssSiIr2K3ksqf1RoZHVrghX9nf98KcjjhQ3fDICvaYgieNgUQUnevlZBYpq0kkdC6RcQrV/r6yi/T3+GtK0CZkjqNYdlsADDmI4ZEVge2a2nYGUzeCEg3xKht9sPHW+SNdn4eBewdy3pXbuHhGprPeWCmoeyfq1vcVT33F+ipUccCt0+G8LtLfo8mYxkC1kaT0c5OXqjA8/AJrTMvO6y03psdziNv+Iv09wmrc5T73PtPwyAqWJ/vTJlp8/RLkULaF7klCZuujedXM9Ec/qfva2pk+kXk3TF9Qp/B78SajlHKXCCfOh5Sls6cN29lM4X/NdY1OT2LHvm9nXQrOVvwOtV7P/ALCjOnzcuD4ZhPe9MQ9frwyPLIC8x/aEI+Alg6xlGjE77WIPQJqmg8o/k0wt2uoYZsOOPZwRt+RLXUDmXGhP4/wsIrBfFC2gakbu2+D7vG4F/ogVOE/qAT11Wdy4qVHo33PPfdw0UUXcfPNN1NWVtaT+kmTEwFBON65J8or3DF3jve1V7zGRBraKQHHKLrpnqC33zPL1iGCOWwAWsMhFCPlcoqvfuQ5zFJ7Ad4jL0UpsNAaDtnlpPWUu3LLi5a3U/add308yXuSuexpvX3ObE5urzjhO3oic8rOZkIrkjs8YTUVgWE1zu/nCZNFSnAMdLY+SQVE9wjAxs69OXf5E/XBbP3yHJAKZ9OPJSvf46LzJYqiIKXkr39V+FxqXHB94Ksz+361xSrmhvDHvGIq3KjDQlPSbhbxuFC5NWLf718Rgt+lBnFV0WTiW/uDVNgrZ7lhfMKwURNS0u3/i4WZY6B9oWfWOEDtkUUsuz/A4JV01rRXTDDSbcjeMM3jfJbJHTTSRlJa3KMfBjTapKBlYhmTc4yO3Ufdk9vsxDQnEVL85DaOw+Gc6AQ+Sqg5HPFBjHYnm3u8XlQz7rGlaGUZo1kvZzHi4ffQytrcK6k+D8OWO2ZRdt97gak8pUgwvsyiYdd+96QN0LBzP8Pq8rptVw6E34v53115R6tzovVO57J6NNp33XUX7733Hj/4wQ9obm7mnHPO4aKLLqK6+pPNVHUiIAjHO/d4vMcdp66xnhy3QR7Tm2+Z67tz7IoH+WjafzQ6QXqVC5bneI9vvmUuqUYNK+mfLEVfepSm/74Ls3wIimERbWzN0QkqL6gvjvUZepuCNNrHJBG3x1ZmOcW4YTUy10kqW0JqCFWZyJLU+h4dxXrTtsl9SpkQz4So2eLH26MvTeKtCz5g5gyFN9+W7Hy5mCfE+y4Ll7c+oNs6q8QFvCr+zFAFFqcERUA4fJB9pj9kamC0iXG/W8mmeReS2NW/63mRvjbpKjZ8ijqeF4w4R9ROdrDRF4ef3f6g/lir/dnNmjZYAUuzDWT2hmk7B9nNQaq1vbxqDeUxNcS/IlkUiXDR/Vf7jA7YqTl33Tc959rHdzcfNnlaf4PZndU5YzlbnRzodC51BankMtp55ZOCehUUZFLzwePgj+TI+Y4a5bJZce5/bCVgQ+QNu/az9ncbubat4hNt/8kq69Oo85OGx3sk1j3rrLO48847+fWvf823v/1tmpqauPfee3ss+P83UXQLNWpS+btlnLV8YbdJ6QFUXeRk1DqdpOn3tsE+1aUkUkNppIZReQsZlbeQiNqKgun+1NJIE+1ckldJM+3U0kh399rT9InUGZWB/NsnQmynwMzcmSUq+ctKBSHsU/bF5hhK6J9j8HojITXELqsvO0zYYElmhBRaUgP5QPbnkGlvFtqEIDmm54iIbJmijg/sly+oUyhRCxiHHfJVyeBet/+gNYjzNKg1YZoGe6y+LEnJnA3TBLWYr6kX0mCO55vKTI6cUcxiSyLmnU/T3kOs/d1GHzQujOCIDN/dvKLSz+w6qU2QLDfe5MqQvXG9MmRTsn5aMuaRFez+wXm+sFKR1LASmR/ZhQ/td768gz4Nz7Dyyf/h8Pcy5D+fyekhPZ60H3roIdavX08ymWTixIlcf/31PPDAA59E21xRdYtQaXMOBB0kQVD1sehk6419vKZLQx3kMV35eE0O6UhXdUoyBCvHC6E4VwDgh/cVCAz5UnSLUOlhxK6wj+RFSyYw097jijQJJZsRZLc/zX3cQ5pAt00y2HvWLU1X7IYaEkXiI6rIdnJRdNNODO16QtuSTfAwCjgnMQdSB/mSepadScyIdnn3Fkq2MkcdD3QfgRBKtvr6I8gJx0qkfOMB9jWCBJSI6fZ30cvT+IF8l4MvlwKHuYzR6We0v7sl1EDTmO0Ih2w7nZ7yjK0VjO0o9dV7tZjLT3mD74db+F5iMAMoYB4lnrSbLdx1bwyrM2lTU+qmTeiSNTVccp7dee4ex9cvTgiSAXNlf16iOUMSoleA5R3vYP73KnEBLxovsFH050XjMF8Ql/KWuZnZaiVBG6vZ6iQUTObvns7T+l/J+0sb2lNbua6t1G2XRGPb3bNAgegwe82Q0t6k5LCxpYrxe9J7nj8LEZFoFChNAYx2MpDHvCc5Fj3vGG2dP4vk7lim/WH7b4ld/dyxqVxQQ9SbztPD137pzCRlT4yns66A3nCPZ/dH8PzvzXPmlv0ZPA7+d6R7UaTs3h//2muvpV+/fkyfPp1p06YxadIkNO3od+rHIslkko0bN6K3X4NoMgM9tLMz2fQGnu0thHsi9RTVXiiltNssU5nk815Sk5ORdD3Ia9yRFannSTUNJlHUj6LbHyW8ez8ApZGlrP/9U3SUj2bql75AXmOry0ns8BDHHrNDTjbfYqcJtB3RBqEPb/LV0T5fY/dWv5NK0DOkfj8UWa4Tum0fyj4TbWSMYfteshnR2uaQ8jA3rVZf5KaIgUIwD3K2eHUcZ7UgPmeHh9r53LsYefWd8gRaIB1k0DM64+Ad7y2iGalrLNLf4x/aL+Ep1jOV4UxQi5G6gpKSiOgi4t+Pc/EFHoe1v0ra/vUShokCIJNhy4gJqjrLeYdaDoRhVqqCs5Qz+FA0sFP5kLPDRxgj5qLmxUh0aG64m0NTKdBQdYu69rlYkcEMs4KpQgWa2z/vKn1Z1qFwM1N5ivcZrRzkQmWuO95OnK/m6bM9puBBo4Co7MuFjOIdpRYpI1wWnci5yluE1Q5fv+9JzKVIX+3GC3c15nWJuRTr7wYSk+yIV7Mm9JrNYW4IvhDSiKgSLWDMg+bGEyZcrWYc555PwW0RP1jZm7no8JhriaacurLnZo/l6RCqiGHuitux292s5r3hCz8e7vFs8fK1Q9eOaifa4fdkrKEnuywnnl9EB3FOw042btzIxIkTiUQiObo9nrT/9Kc/0dbWxurVq1myZAkPPPAAsViMxx57rMdGnijZesclhGMHA4lOvHfLp7psu7uKRF2+7T38g/PSGXbwkZqcDHG8xhXdovJ3y3z9NeSrC+jURsPOZpSk/3SpGknURCd7EzPJNxpJdNqLfE4yEV24hDJ5FRal317hq6M+WYUhjwLujSiYvx2KqIjxkbgDgNBt+1B32zvVNWIzA0NJlhg6l+u92+V6xSFgCaJnzP68KXWey6ccpL83WUVReFWaVzrjw6BgoSttJNDcU/i2u6tQVGmH5iAxhWQhm/iX0CWMi1+OolrcFzlAozEaQ4L14GBCP9rPiMYwz9UoXDQj47C2fmk+M0U+zgmzSO2LHlI5KznUzo3NSJo/r7BvUSuNZhtSsegvB3CRphAJ16BGYggRZ0+ymqLwKve59iarqHhwFer9YDV334dF4VU0pc6jNC/GzYr9nDeIWfxUvsk59CXt4M3eZBUlkWXkpU9re5NVvGPuY4iyhwpK0k6CI2khTnGoP1h+HuuSNHrilNMTB7aiExiqF1ZV+hNhSSrJYAWaU3MoCq8iDytnzIvCq3gh2cYNHj/UeX2i/PJQnH+M2Y5zN/V8/RgoDo/5MLGIptS0nP7vbm6iYyNMaeMcexhCFXboV8f8NA/5UUg2X3hUtUh05q5HXbanh7JVNUZnZzYzzGeSLV6u+XN60O0V93hTUxONjY00NzfT2dnJ0KFDj7eNRyUOuUqk5HDg55ZnkiXro0RKkoF6R6PTK730PEw1hgkXZ030rB1vsj7K6IdWuP/2/u4lNTmZcMyYR1agF8exEprbxqZfzye5pz+p0kEUfenXhHcfSH+kcc43brHjnZMmKfoRjh5mT7LaZmuSGiIJdPqfJVnfn2hpG1JmYr2HR15nt3KJj4AhEB5P/8f8ryIQILZ0wKiw7333egA/YyRJWqAnMrtXKVWeS1lcF/ZPbS8ZxHA9014htRw97+fe3736TnnO5zJrURoeWYEiFLfPUrKfS3JTe4dgwe4ydCJcz2Sei79PI0f4RridHydbuS3dceX/+Ad08zBEFKbuKOAvbx/i4gvgrZWSWe+0UR59ya1vT7Ka/pUJ9m45QiM2DenQ6aNYt6mZcLIJrQ4uDbcSVjQ7PeMQA1Gnuu13nmu4vgLja2AlFSwlhQxrOX3k7Zfh+gpSoj8fhw7xuJF5Jq9D2pS8VYSVOJ2d/QnLJENCy1EUi38Kwa86D9EsjmAqFp9nGFbCrjO73yUKwyMrCCtxhNTc/s8m3pEoRH+hIX6i0Vlv6zyXMrkuHEKiUBXqy4umkZ4fdh2dnf1zxjxhmiwSA7kydZBoKOSO+QYZ5eVkC4WqSkgJ5ThQe+dZVyLT3OPe5/T2vyPe51QjmTCuxE88xtl7JRUhhxHNJ1nc4wDDed13bZUS/QlHg9dZ7zzozXO6c0OZ2jX5Cp/B40er0yU8vn37dkaPHs3FF1/MqFGjmDFjBjNmzGD8+PFB6idFHHj8o7/5zSdCrnK0emMX1BAbcwg11Avv8VMAjsnmHt9yRzXxbQMCsyEdb51O3yhqbl90VZ7U7RO29t39qLtTaDGdxMOFaN874J6yl/Emt0Yy95JPJou5LHaJW9ZCfRWTRB1v9ymkpFxziUsUTUGYgqHbRlIZLzlhz3msOiuEHYo2WR3KetHAy2xlHHCmvp8NRhFXq7NyynrjgkX83X2H+eV385n5ei5Xwp8jmzhQlOTWBVfyzD2LOfcrE4kVxHh2/qv8C7N8oVhH2/7s2OQgvRViBwPpw5nqUD4UH9NCnCp1ZI6eM4a1lsIrqQIKZYgm8pnKcCb3Le22XR8//XUG/fsf7WiGuNEtW54W01nVvp4CfSOHjIk+LvmunnOjaGQ5O+iDThUjWcFHtGMgEAxXBnCuHMZbfIRGKNBD3inLQbV2/fv5OXM/u87uJDtrXpAczzXeyVyneqt3utd5osv6woHfdAuPd+k9fv/99wOwbNkyHn/8cb785S9/ogb7dJCt86uJbysgsTvf57VpJU4+n/jxikhqjH54BZHRnaRGDEZEw0gPDZrQQ4hoGBENs1x+eFRlK7rF9q/NIrHn6DygzQeLkKVZmKMhfahFYHrLtPzxwh2coTdweVhhiDjIlHnN/MOD9s83f9nEuTc1k5/o2QHxk5CZVLCFJh4Sb/M++7iJM2lHZ01qMFGlkYTIfblHr7yAHz4Ao96YHljm3yYnUFhRQFNDK5d8dwarf/kBb/7dX9M0qht4QqylSfQMcb4u/OPtxiaLzd1+7wJGUEcrr4lt7OUwMynPKVPBYqC2n3xVZVpY4fuxVgaHD3K7OoUJanFXRfvaJPWQO2dL8pa7EQPORHEc+LyojAxvwRSZ659sB0Gn/IlqMQXkMTUN20+lhELyOJszqEx7yJ9Lic9D3ltWrdEYeA11LKLolu1IFuBAejzy5CO9oWb+TE5V6REeD7L0n4acqnDG1vnVoPZMNHAqtF8YmguNb72jmkRdPo2/uwtFVXLg8eZH52OOGMyWZxZQvmALq+JhppAdBuV95gz38tjfrnAd3+zQk955jIbvbCT1m2LXTdZKpHISiATlY7YSKVbn7yV6pJG/MVOgqNyeEix4K8TFMyz3Hnjd0nxmph23ettnx6PXnU4tzcQxGctgBqXJX+KyHFPdwOW6ws+TL3MT1/jKKiafitdmuM5nQXLtWxWsXmvTqPZtUhlvFKfLTlIr96YNjUxDqkr6xxlHxU8ekpiQRUgSHJu8r6OFpXI77STpIEEJBezlCP2IcCHlbpmr4uH/x96bh8dRXen/n1vVXdXdkrxKljdJtmyMN2xWG7MYYxuzDSQQVo8hgGcgYULml40MmSyThOwzk0kgEEgICSYkjFljFkuWF/bE7NjgBduyLG/yqs2t7q6qe39/VFd1VaullsEEmC/nefxYap2+99a9t+5yznvew0Xmjl6Z5wr1WbBN9poVvDFzNVoqg5pp0H9NCi1Vz/jNgvK2c3w3RNP3pvFE8+ssiIY50z0u+SB/+9+Sb4YIU+YzlaW8yy7Zjo3kSqagofkpSt14/6Pw0mi64VfTONRYwsOZN/ntbyyMockeQq6yEReyEKpaBX523Vl6TGJW9775F4rY6EnvyQcksf+FJ/tLzp/f/c72cTdVfxh1fmTIVTZv3szNN9/c4xf/nmFfmulQMj7Vp4xb8VqnqF5fdI60Xm86wUxfRyqYXxgOibF2qE4tZqPHXITOxAfqAHj7ui669KPQtzSj0t4iDsNuuIPmez5H2Z/vcRds8Q5SjAmZWD0Q4PrsAcB7zmA9bnu7h3z1/gw990Uh+kw9EeXA6J2M2LyHMpGLwzVWOTw7E844FZ55oecbalAOhyhB2l1utq0erCq9leVleJJSUic2slu2o4TDF4w2NE1jjG6FwFQe6cLE5Eif4MebN8GscfFah5PWDWPaRsm6km2smfQaG4VAIekgw0Ps5rwL4PTzdNZdc0oIGNnZWIJychu0ik5mFS+wIJAs5H71AnPVTERUst7Zw4RoBbW1BlVr+zGQOE+zkfFUMFQvYaZZhS0zuTLFO9hGnFN+fSVLrl/MfM3hKQPOrZ6MbLaRaR09ZiCTXb7JO//QEOs3mNMHvsjMU3N9vup5QdkP424fOG6ExMQH6nhutiwQopW9GWf7yy1/XehQUse7bDR28esn93D9vGEM+cwGppyWoePXg/neXfv42rxRdHzpKYY9fhqppjKaD6a5Z+d6SobbXLZrCj+/dR3paIQbfrgXzZChbH/eOxM0VXvvjzcOyY0DEFH3e/Fap8d4a08K8f0XEss22HW/zeW6xoP3SzKXdC+4RzKXwBzz5lmxNc8bz0/IVQ6vrGLS46bdr18/ZsyYUbSAv4cc/btlPYZ85UtfknwUCksqJEcq5CuYsxrCYVcyrbsv7dYjy9k77s6VRBJGqC80ozuktuX3N9Clj+NU6zPsWFBFutHdfI+65zmeunMr16Y7QQgu0G0em/oYN/ww0q28cXeu9MPX3L4N3wzG3bmyx2dcL8N81cVSc/bEzjW4cTg7xmTo2LiDMuFmaUrWjuDFhoOcPqOLN5b1Y2aRW/bhyrg7V3bLm364so49tIhDRFUjiv684ggmINnsRJnWS73B0LFxd64kMe4gytL5a0M/BiSnse/iFzFUG+cME5wR2uBsfvHdOFWTpT9umuEw/ncN3PllxafeUPgbdOYZBsd2U6aCbGq7iQ3pYMQtf+XHNwkufryNprcHsf1rpTRagsuYwrNiMyU1nfT7jzepu1PmytRtHp/YwQ3Vf2bXtYqlv1MMuxjelMuZ/+sIv7yoitn2qaFMbPkZxh7505dYN01x+in4FpR1bys+90iKdQuBre5zPvmAZKel6IjKbjf6cXeuJJa9vRbKYHY+Z/Dlu3aQiOh8c/Sx1JzvhjOe8N0ouozy30t3uJWc7YLGNBymnncQlhzDGDGY5I5axKfWUFURhTvCgDpwrU/2njIiQ1wg11EBnaPuWOlz+RtDk6S2lhMZku4WquknBwkkBSn0WVDuuSXFhWlAwHlp+N03YGYPc6ynOedJX9bZDQvnYu35aFhq/y9Jj5t2RUUFF1100d+zLT3Kun88G3mwDZkq7ifWYgolJb1lDy3Ej11IDsfs1Ju4ZA9aQaKWjZ+f5d9S4ciZUAqZ7fOBaJ7YUvGM/hCV3IaBG6f9rP0nKt46n7JsR5YJjcgrGm/OceeEwGH8ogbitR3uM2Q3rkJ9u2HhLFJbw/5tJ2VhSxniqw4bB3smV/H4oWdrExDYRNKtXBxZyy8GVPB4JMoCx+HxaJR0v6Gc2DCRW80Gjls1vUAvBYkxXPNkMGVlz+LqbVg4p5ve72Q912nzfB03A1f+jST3nUnaUCZpw0jJY3gj8ijTohr3ZRQXyX9AaLkbWiTtsndJStm4cFagJJuNC2cx/p4GNv3Hify86S2+jMFt/2lyMxfx4rzlzDylw9/gXqqLYXfGGZzcz4brziK1tZ9PwhF9TVAWyY63pjFA7GF/spIOs8Xf+Bq7ylnbuQnzn4dyCSP4zlnbSeGgA8fjmuI7ZJrNW/bTes1sIvpDlBnZRCBCI/KKpPMfYHRqLs+LVcQsi9gj8B9/HMQUo5FXnAgjDJWNC7Z9/7fXJuNtyfC1c3h23ArOyCLpa/84nf0Prkdl3NSpGVvSeLfii1HBgzZcZYSpUTcunMX4RW4YWUGMhOPqVJkNqIzj93dU78Jy4n5su2fOrjIbOD51An9R+9hFJ7aUnPvwmbz1qBbSDRHwBN5NL+WrO7Ncfa9OQ+8k45R2I0pJ3ghym4smV7EUYkjgs1+BViBNRPRNGbJERd6SiArHf8cA9HQHku75FoJzLtiuYhLV9+NQSm/v0yfm8cPT6XHTjkbfYxDiByAT/lh3xG/akSGB0Kf3UVZf9I66Y1U3Ha/uo+5YFSZX+QDNMUGfticqLojoAqcr0411ddcxJ9Lx+vOUCeHSXyqFITqoNFZjaO0YmpMFtK3yv1Popl1lrgpl+fKIQLzcyl540OxbT2bI9Eoi1+9Ga7LQY1GGO8sKZvnykMxV5nLiJTayK8nNL3dw9/EaT/9NsdsZwmXPj6XKbOBQ/bFUG5U4diavXcsxtYNsS80jo/pTZS4nkjDY0Xpsr9mKqs164iU2jQdPC8VmPypXMdM4yKOZVfxrPO23S6LTnJrL6N+uLngz12NRYknFLquSp0ULe6xyYppBlVlHVDuEwEGPJ9wxLEQoYku+ce1oDDZyGVNZxFuUk+BPvEh6ueC5eYqZpwqeeQHeXTGA+Uzi329Yh6ne4Cw5AQxB8l+gy1F0aDlfc6sayj9Gu1hiR5hvSJbYUT7FGTzH1kASlgwHI2lOt6upYyN7ZCdO1ObKEVVktsEBpyKEQUgpd6Op/vEqPrPV4rGn4PKoYK9qYbYOD6lGMjLOyNgq6g4dxwHC3+9SUUY6ERbXaZw+Q7K2XmOB8Vro8HXrTe18SRMYmsZAHJ62JFH7KDZxgPEMYdw9q/wNsBBGAmCkuQpD60ChM9JcRUtmGo5ZzkjnqW4ZvRQR1qh2Wujks4lp/CG5mnc44OcUL8QWFnw3FbpPrhGc51VmA5GEgZbaR0S4ZCVagQB0IdzE3692wHHgp8+N/0pHBDbv1FE6Hest3xKVmSxQLRFUYBtwzMGoAmuL1z5PeiJhgTCJimMOhk/M44dVVjHpcdO+5557in757yW9kavky8fWp/13EI/cIyjpe8tgjEuyEmnKvYTDrr2D4+77Do9/4dMssJIssSOcbF+ApWlsT88CYLxsoOnz00OWgp7IVSzlUi6CyOVWlrncyvtJMvLbGaA51D4vj29PNIn5cs5Ls3na3sqntGP9749DA3fPC4UIeWQd3ufb07OIRRwyyihYdu5GlL0hIMjIkQAAIABJREFUCZkFzQlSMsOwaAvnRjX2qhY67VL/vrI9+/zebWW93MV4bSghWlYcLtZOY5H9CNfpJ2Ipty6PxCReiC0kKzURjUn2AaLSNc92ygwHSDKYBBne4eV6xWkzFC/WwWlyrHsbTo2mXbRRrlX4CP1y+3iWiDf9DXqOcQZ7UhZCbsi6I8YT0XTOkNkkLFlQ1kVmDa/ZLsf7VUxlkfUmr29NMUmD2fKULAZB8qTjEpIkb4Rt6TNZznIuwQENPh2BP1lwcdRisSWZZZ3JYt7iy3IGS+ynmG84LErp7FTV7GQro5afwPcSq9mzYgCr5USOi61FCEnKyTC2K5cX3cspfoWayE95gZvlwNBNuxBGAi1H5CKE9Od7THSwPT3LZzHz5sH29CwmaDCBamJ6F9dm5x495JguNK+8OoJle/Mx1aWDyzHUjeQkeSNoccgcktzeNYi74gd83MmGfwoTp5wWt1hiPeWOrxXhpJcuIK1poZu2IdKkCrxrXvs86YmEJfdMdq/l9aVfPpHCUpTG9MMUn8a04+IcuUqRcbZ2lxId2vm+dY60XlDHpTHVUFkA0/rrcuQqfUGi90mngF6QztSTZ7TFpFrKsSv7h9DjVWYDb/z+D+z70kIi+1oQmJwVCQNXEnd0N8NZu0sxq9tCPrWOhRpyq/BJRgAkijq5AR3Nz76l5ad01ARV0WUujWngu0GpNusw9U48tI5U7obs6bv+0WnYohQlBVXmcnZnpmGpMqrM5bxtH6RcuhSZhmjHFqXsTJ1csK4qswFDuP1ni1IiqtOv5088wpdMy7+x/SIN30xEQEm/TQC2lHw+1Z9bxEkoMaDbcwoctqWzlJ3iIAoNgUR4fZMtTxB2AUkpuT01mQg6aaWIopBC8S/mWrbp8OhcxZlLYUV6CjE0UkhuMt8iokfRDImdkdyQHMRnIvvRNEG7dTTH6cdRHV3K7szJvCBWcYXhMoe95qR5me183TzAzzOCs6JpTo5k2Jaey27xItOMZLZfyqg263nOacUGogjmRHON/t+M5DIjN+YPZiQna/DLTAVlmsHJzgTWiHW0KDhO244upyL1XcTk0RwjRvCU8SbDrF1cFuvwx/6HKcFwLcOlUUGZpvFOxuEeq4JKYTJFTeAtsZE0XXzuv/aS+tU5pBr78QrrGBhZS2vmGE7QxmfnVX13+lahgZI+VWpUdAIidBD05oUnXj94IWWuhUhk302ZnVfL/Xm1LT2Xocbq0DyLqrYe3X1NtuQ+qwJTGUxRbn+lyHC1sdfPOR5sf0PGzo4FnBmJkL+g5rff/ypOuA3ZvigkofneQ3lev+T64vDXs/el9xEu6zOd974/GtOPgmxYmLtpF6MtjdRaKNnzraSvOkdaL19HmLkJH7z9fpDmGM1wQqhu98MIqnYIlVfc5m/Y4L6kJ193IQCLgcv7RX1Tr8+LbIIeK/SceVUgkUIPLQ8bIgdoyYRzTXvmxGD7heOgCQfRC6myFov5ZmMtmxja0xc4VMcafHOe9zuAIy3usAZxV3ypfzuJxS1EqpcXS7hlxuIWdlduyRurW75v9N0EmBMVD0QVOBKQJKVi3fpKBqYGcykj+F/VSJdSnM1RDE8Mpjnp5gb3NgovV7j3uxZzO1p2Jf1nDMorjqCVncyLd/KHZH8WRPay3BnEq1JQqRTb68q4PR0hwl5KiQIa/5Yup1QI0kmHwSLBpUxgjbOOFifNJWqIa6rLTpfPRGNoop3qWAPVwKez9V6g4MR+CWSXw9/EU9TbgzkxaiFQ7maktTNX01hsSS7vVxIy76eUm3UsaDofpEOs0uSYvbV+GFyNvoZ1wLdL1qKk5PZUJbtVById4ZJYGx7dhCYkppIsiAoeyebpfgXBiXoGU47PlleNra2h4lsaUA9xODqe4MF2xRUlawE/KXeP86wmVl9gWkh//siu3Ds2KlZHgyWpBjY7itelw3HiePRYtCCv+6hYXeh3t7wem0RNRONoIYmkR4fCBrtt2Nn2z1VJFluSS6Ma0H0e5be/Jwm+c93+Fpif+eV5XOfVsQbfjP6JeTysU0z6tGmvWrWK7du3s2DBArZt20ZVVZXPNPX3kIl/rEPX2vqE5P5Ejry4L7grnrn2vfAQB03kk41hTLAHA8Fc00dOhJAI5RJuBDmQq816Xk9N4knVQolIcylH8/NU7nYyupcym9NzETg+j/b2AKf6JidKh3Q3biOlmHOl4IxTXdQ0wDPPS9q+XsaoLEGH5xJ4Lyk4PVlsuYvjpVGXqGRatBUtnmB45jh28iLfTrQCgsWWYrqtE2Ey++kKsJbtwNHWoICozC36NdoahlHiG+SFyIGugmJLya8yg/hWOsmdyUGYmFzKBH6eWkeH2si8rumcFH8RU2sPzSFProjib66PZrm8HzJNLll0Cc+ev4pdqgPHtpggWpmuaSzNRBksMrSyk28MTPHDgzFecQTTtFx71qhBHJL7KReCpZbkHUvxw3g7t6dasznV3bC6fF6pQu07EpKRkn0KN0mJkqTYjO1MQaewC+a9yGWJLn6Z7v35gvJBPWtfJJ/rXGD3aELPlyOp91Euq5gU3bR/9rOf0dTUxM6dO1mwYAFLlizhwIEDfOtb3ypa+JESpXCBUn2Y57IPen3ROdJ6fS3rg0YrdkvBmLbRmnb4prqQbqgs94ToJW/w/pZfnvec+Wc673se6KvPzxntNXFRqG1BeoqRxgo80+VIYwWWHMAucRIKnWnx1TSlS4jKydmNqhZbe4vqiOaX1ZOMNFfyuN3GpSmDkcYKtqVdTvWL5D/wmP0oVxnwekag6hUzAwk+3l6muFiNYQktAYKOcbnnzEr+s3q/y1Qqa4J1pcGStDiKMbpgmSWZE4kgkDipDEMjDXw3NYATop2scqAM0EQr60UrSgmasdgj28jg8LlEK4YuuL3NXfRTyuFfjTa2Z3L97/Wlxz29zZYssgZhYnASQ3mwcw9JlWY6uY3/IG1UamUodM/t76cd9J7J0DTKhWSpJVkbN1iRsZDzZ7D5zR3sHp3mijvO5Tf/8EfSKcHNpss1P1uHadFWyGh8O+Ei/ZtsyX2ZCkwM5jOB/0qvwxQZWtUevmvmrBBfjx3gx6lBvOwIpmv4bgYnlUFlfw6ag/KvJfntLyQvt6c5PnBZcv307s/nR+B/LYdV2irmps5CRXN94493XjuC87Gna9LqzhSt7OTfss8XPMgUeod7k77oHK6eCpjL8znloXe+8w9K76NcVjEp6tO++uqrue+++7jqqqtYtGgRAFdccQV//vOfixb+fuWjzj3+YdX5QbZ/faKZXeM2hywpSilGNk3gqIOVfSpr7G1PhXznyRuhaWPx1Jx9bb8X2+3xYesJg+HOUwVR5j2VJaWkjo3dfOorI2s40z6mxza9It9hoLGWVo7lBHtc6G+PylUcY7TwdmYoJ0am0P/Hy5h5qvDTaLbari/Ycwl4KTiDbas26/wYZU+qzTriJTabUhcxwllCc2oaL2rLMTTHTzN5oj2P3SpCnfYupoxwIiN4he0cFPv5orGXBzPQxSCE2s9OhnB1ZA91zmCIWlzwBclTvzKJpSUtQkMoyQ3a2X67Xi7byY6aLkY0xTmpYzjgcqd7ZCrncBRp8SYHGUVE6aSRnMf4EEbBewbZ1T2z2GJLMrB/nFdPHsM/3Hye/50dW/ay5vrfc30A0b7Y0rnOVN1SQz6YKiPqTOUYbRhPynewRBOl0Q4+H/ChN2UPjNVmHQN/3c6G784jtbV/wXkWdE144tXp+bS9cfLGLOmU8TOe4Wuc4btbXtUf5gojdyB+MCORRJhuXnlY72ah9uS3KyieGTr4PhyJtcXNRz8NJ17+sVjPPo5lFeMeL3rT9vZ0bxF3HAfHKe7z+EQ+nlKWKmXk/D15jFOK5HfDJmyBA4Zy8SgeqQOueWfj52eF0gfmvtM3NG23egIxrl5s9xy5J0s9qXO6GBtCt/alnnXsyaKdp7CIt1jHbrrkQapZxytSL5hcwpFWjj0rswZHjkLXotlYbI2LtdO5J7OchdqpIOH5ZaWcNqOTN+sSzJRljNTKmEw5ANdwQtYPmm8ezHVaPlrdEB2AYrl4laiwuSgbU31+BP7ovMpcMYtReoIBsj9jtMF0yjRpbSc1EY3Pa5JHrAPEheDbxn5A5xRa+VNGcMydpYxnP49GBTcbGvdlJGkrxQCRZtHM7Zxw3RSmji5nR+M+HvrdGk5fVUEzrTRykMuYwio2k1IapWIn344f4ObUINapQUxiaLdnAKjOWl08uTSqQTLNm4cy7Gjcy4jRFe6G/YcXqJKKskgujjohHAqZfseINl6kjR2yjdfYxSjVxtf7lYU2s6DLJHkjqLTsxTzb813Gcxd45TXbNvc5GzAwsiGMa0hJi7MZy0EG0yFzce6dSpGW5QXGuLDk2nZ4eOGgGfq9ILl7alcQSe+VtU7uZYJWUaT9xev8qErfx6h3+buZx48//nhuueUW9uzZw7333suyZcuYNq0nrqYPTj6qwfAfRp0fZPtHyIE8V9+P009pD3N2p1zOak9GmisZcGc7Wg0kP++SOoBr3hl47wFSP82lD5TpHNWnh7bu63NWmSsxxUH+ljyVJ9ntx3Y/z3pMq5xysYYBdjMlsQ6E6I4e99qcq8/9fZI2lHPMtZjicb6PRkZu5THH4ZyoxgNqXUGO7ZciS7gkYuOyZ9k8rB5jljqH3f2eY81ok7jWxjHC5m88DEDlK4of/BQ+9dIhamKPE1ykgihhz2yWj9CtMhuIik4EEpnSGMkToEkG6BKByCMF2c+Y6KPUAr+wJtKiXPP3F412QKNM04gJ2Q38lVFgaB38Vxpuyh7qL4rAL5zHODFWwQnXXcCI0e4mM2J0OVw7mW2vb2JQeysj1USfTGW72M+XEq10SBgnDCaJof4zGFo7ApCp3n2pX/nbJhrW7uDZ4f05asdBvpJMc19ee9PZKeiZqsH1ZX/bNhhDCyOEznx1LKvFOr57IMPV0U4flDXSWBEyPXtm/0JmS5H16BcyL3vlBF0wk+QBonKKy4im0tjaGk4xt3GilDyW9ds/aUNURThFnurPx5HmSj9qIDh/PbFkf4zYgV7JooJ94Un+wch9GA3iMuDayn9X6NaunsTrM1tKfpTqz9lGW4jiOF8vXwrVWUw+rDW0qo99UUyOlHm86Kb9pS99iaVLlxKLxdi9ezfXXHMN8+bNK1rwERVDoB2dwNlSHIim1xbX64vOkdZ7v2WJQLarDxptWbtiOs/NW8bMAGd3vk5zei6l0TrisXZ2iLmkyHKPj3YoYSUld+SAaocWShq3nk0m3a/XZ1CGcNHZ2WfVY1HQBUqPMNQqpcrq55N6JNUoP7nGI2YHV2fXi9I7JKmfgLbRgZSiprSeXcmTccyKrJmx3jdregtls5rNCns5n8n6Hi+I2KHkEp4khBXaKONCIoRiYEZw4vwDWetEDng2+CdxylelGI0GpmRbxgtzcc2XfuL7hIHtWFTHGzCtg6E6mzmLoeIl4ln+atmVpNUZTFTbF9rMDjqDyUQcvp9uo0vspIoDNDKI5yw4w3Q3vpQGV8bhkUPuJvKYDaeqeViR1YzSW0PPNlqXrB1Zy7mjwzeoEbUVvFmzhgvfmu2TqdhCUqEdAsLMYwDb0mf7G7c0XQS/Zjgs7pJcGtEgCtLS/Xkxy+nHnHd3uznb0bksKnjMdrgiy2p2kV6KpBNplrO9cw+LMoPQlcYgbAwijFGeX30MbQPbqexykHT4czaTDQMc9LuDbPjmXFKNWfN4V24urpd7OCv+FoZw3S2VxmrfJDxcuuQq3mee6fnYrLtlF53YSnKOcxabs4cUJd/habGWfVJR7kwiokX8+e/NAS+c0GujJ3rCYLh8qtdNw+vXYuLp9eRCArq1qyfZa3TxdGodJhEuZQQ/S+0ghd0tXWl+3/ZWZzH5sBDfzcne+6Ivz9hXPT1hcFKRcopu2nfffTfXX38955xzTtFGfVBiLRqOFBaOLH46klq0qF5fdI603mGV1ZXOcghr/uIX+exOP6f0e5V80n8oTOzi3bZPm9HeY2asoMlo/O9yp/qeeNjt2yrg1gyiyeqRKMW+eyhqtBF61sb/uhJVEyFy/R7OaCyljo3slG2h5BqmlJQ+5m4AQoOSO2BC2uU7T3wL9O9L9L0ddPWA2B1/TwN/vaZ7cgnhhHM1J5VOh3JcRikpyUwCrR30rWfzen09pwfoQt9epvgsEWpsDYlO4ucwviYHwgmy4IGblnRLzdWMuWkRvOt+9rCVZPpdFfCzKOzLEWBca+5ncQSeTOVucHPVCXTaL3K0rrjKaGVRBq5mP0uJApInDfjnx8CMaQx/QPL03ZI2pXhz8BbG/0Kx9WoVOgRsdqIcvaOEHY37/Js2uH7mEU1x372wgGO5X7xJyqliqdUU4oMXWT4CjyAnpjmkHJ2m856g7OE0Df0kF/63xoZ/n0N6RynK0tCFyQi5JOTXV/Idn2u+RZtAiwW6MHgzvY1dvEsJBgsYSwMbaaaNnbIdzXC45YktrF9wGqnGspA5dnv6TErlssJzMOt+Gds1m6imUGi5wxX4P+dval5/fDbuMqIFQxg92t0ocIKWnymPguUV+5v//hmQGOYSN3luKu9ZuyX5yOphgpY1mQeTFR2ODI30p4p+IWa89xsN8Yn0TYpu2hs3bqSpqYmamgJktn8vSUlk0yGoKh6hJpuL6/WoY4qQm8VFzxb3JfVFz0lZLhlBkYw9svkQke/vRdtmY901FFWTvf4FgvLfq5lo3J0ridWET+zrF84l3VzAR7jiJG6NL+f4VSchsLtxEru3J/cGE/Rpyz0uD3JIFIz54v3uj3FoTs1BpaPdOI5FxoaUQFO2X2ftTX/B1DrYlprDa2RooZNKOtiP7SbXEJJMrSD5eYh9PUf2suG6WaSbEhy6UTFCawARoVmcgcc37uVfVgBpSBnQYec2rVangipzWeg2dYme4Emrw98oL98AuzNuCNjRKyfz3LyXfOvEtBdKcIR7qt6emkXmun7d/HjewqunOzCuT4b69BX5NoMiGbZ8/j5qoxoywFYT1TRGnCdZ+5DiaUtSIcDQFL92Ovli1sT96Qj8Mg1DsFlqQbkFkSYNVQNz/wH+/W7Fd0ydB61NaIPgX0fBI1tdnu7HbLhcnkbsYJyHfv8iXDPD9zO/eu8aLumoRWiSSVQCgmu0EwHFikyc2YFNadydK9n2vROo2P0397lUhOjtGV79Km6mqS6JPVAipMP4O5ex9XvTcLZHIRIcIzhJG8fyjMUcbRxkP9PTHRyjlfOabGYqIxmjDeZt2Z93aOHTTOIp3ub6iyJ8KVFPW0SjwpkTogc1tA6/Dj3dwU7pUMdmTHTfL+2og8xTYynP5vnuiZcbYLJWzmTKiVptIUY0T+b4GAkXb+Hy6vfuK+2tTu8ZSn4FSougV9kcynKPN6fcZx1/Z5iv3Gl29YKy7rp5pJpyJtme1pb8turpDmZRzVK2+NEQ51IbYlfrrf2QO1x8kObxnvq4t3Ydrt6RLquYFEWPX3DBBWzZsoUBAwYQjUZRyg1jWbVqVdHC3698nNHjyhAIK2DS/oiiFXvSEzi8bW5nUnoEALESh9Sh8A3ZSxgS+l6Bm/ahhTn/9rb0XDKyrGB5+RIrcaiwV2JqHT7yF9x0iq9HXerJBzIaJxvnMUI+427u2fILlVWoPo8WMiNlNmbYRWOf5MxlpPmyj3D2EM/POW0uo1Q0yhycUH3Pzl3GTd/p4BffKWPWinnESuxQnYU2bYWGnjCRyS5AUWU20JyazurICuYbkgcyGhdocfqXuRPJQ19nZBnL5RrKIo3MN912PC/qmR9gGFuUcljpVHIS/ThLm+p//uLUx7hoQ85icH+6krM4jUfk8z76/VPaTFRMZ22ymc392uioUZQ1wZj2/ig0JjLEv1H3Ns+Ci2asxGHJoZdYEEhA4tVdbJz822O2PE/vDbmTZg4RyUYBDKOUE7Qq1qhdtA1qZ8aB0fxUvcDNnBryuY5f1EDjt2eQbiwlVmLTdchgpdzMYEqYog3jLbmL/XQyWxtdtG1VZgMt1vQ+zWvvOfSE4Y95/t+DyUj6+p4U0+lJL39O9jSW+ZtfrMThlY79vMxOH8x5EsOZrIVP7LESh65DRqiOfPkEPR7Wed/o8V//+tdFG/OJdBf79kr0H+xHa3p/Ju0PS6rMBsZq7XgHQ81MIPM2Y6N3XFFB8QAyhcrLl5503PScR/NrZy1HRSRj4yt8lHBBAE4f6gvGDFcIGBNfUbDdPrtX3ER2JUP16S9Ibv2p4PhVJ7kELKYTqtM7eHibtbvYT0MTCSrMlf4BYbl4lfkBwNsjVgfXUtqtLWPz2re1yzVxH5Dw+0w5SWVyGS615Z/Usz615WubAtmeNI1BegtjjUe5GfheSvHtkj1sS7fypmrnJxefCv2zfm0vgKBtL19/5K9MYWg3V0cwZPCQSlOutSOEw4QtME6UMjjVGXJDeHUXGyfvoOIdsjy9l7oUG2U5SdIkMHkXnQP6IWzh0N6/hb907OWyzBSWiNWkVI7e07BBKOmP09auU5glq1kqNrFbduAImy/E1qBpbxRs22sWoXjsvs5r7zkMkQ6NefDvwXA4r7z8+oLSl3epJ738Q25PCOd8znPNTDBSJrPMeI38IPt/oTq3dp1S8CBdrM4PUu+jXFYxKbppv/TSSwU/v+SSS4oWfiTl44K+9iQ/L/THrf35UgihWkj6ks5UKo10V4yoypughnB9+GnXQpHpihJV3YlaAC4wd/CF9CD+ST8QbpsBWCCl64v2UbkBnULc3QBzoxqLLcWl0d59fJdGNb88GSDDGIXGqaskJxrP+IjvQjLSXMnujBuBUWU2gIpAIA3nAH1/aGOLCVm0/6XSuCni8JgNVxkaKpNhOuND5DFVegSlJJkx0LFR+jdtj7VVKo1vmjpKuYQ0W5049P80YuCwUF0KGGr8ldqIxtLkcYxL1eClMy1LlTJi/p5QDu9nnoeB/wFSpApSlwI+tWbwOYMblYfY9sZMplI02ZKtqoLRwmCKmspyXqcDnXO//y5/vDdGYnCKUY1Hh+g9q3W3X5M3wnC1AiGyyHxjBauF4qDdn6/HWvlJekCIpMSrE3JMcHfpB7ohpouNUxCx7obwhSX/0ClTqV7r60udgPteVKZgV157WBHyafeGcPbIdcB9NyOq+EaV6YoWJFQJyifkKmGdYlJ003711Vf9nzOZDG+99RbHH3/8e960f/jDH/Lmm28ihOAb3/gGU6ZM6dP3PqpcsR9GnX+P9nvoXU8KIVRlGshbL9zUnC2hhCFuWTlpTs91yRnykJRVKpD+T7j8xirV6bOIQy6tZ0mmjCsYx89T60lpDldHdlEViYJXpMjVrfLbL3KfeykdPT71y/uVYHcVP+36SNx0Fl2cdhHFlZ3T2JxFKEdMI6/PhP/84CKrIYAQzj57qzOQDrk/kI5SYJvubbfHdIjZdrQ57TxtWQyig+2ijV3yEDaSf45oNKfPotJYzSUbWnnMlllEdoSL9DIknaE+a07NZV+0l7kjwFaSh9nEr8qaaGk/nozqh20LltVpzDxF+qC8lXUan7EFdmkFlzl7fDT4kzZcHtWptyRlwE9SDl8ZUImW2pfdqPpzp54NJQqMGcBix+DSiGCcbRH1ecUnclC1MqbS4ptaO5l1g/g1bT69541GJ0rkyvDKdMxydrQey7HGaqYZ7YDOtxIdQCQ0b5vEAO4/BCZGlq51PSnSXGUcoDobVlYUyS1cwhMPid4bKnybLVlkV2BKrcf6+lQnQAbs3eVo6e56QcR6IYRzflpOyHLhp4onU9JiMZpbT+411e2RRF/3Ve+jXNb7Ro//6Ec/Cv3e1dXFLbfcUrSBhWT16tU0NTXx4IMPsmnTJm655RYWL178nsr6RI6cNGRe5UzCLGDexuL7EAul4vuci9YOIlD1hMG4u5YQr20PlHUmXV0DCPnOCrQjHyWrCwPZ5fr9PDNspVZGlezHYOWhVmtp09oZqiXZnp5OWg7wyU5C9YnuL4zHI95iTQ+ZJF0gT86M7ZUT8s9m+0MFQreC7f9D12DOj0z0+6wYmYTHaw6KmZJQusiT7QvYnXG3D6/OQmU1p+dyXsLgN51PMJ0pIfa1+vQUJmn9cm2MbGSp9QaaNdFHZHeXwhuBat3LI+mRrCDBpYzgv1p3sF29gYZGKQbm8mqePXsrZ2RBeXLF0VhMYVvawElnfDQ41hQaraPYF/0LQ4TFFF1w98F+7JKluVCiru6hRB4r3YNdkzme8W6olerMMtuNZ8NCF/S1LrKfFtXIcCK0YPNE12lM0oZ2G1ddGDiqeJiTnjCoVO8EENO17CeJbZ3BFqv7vOjVj0vviHFPKrVGBqtYoL5ObGsmWwLjVSxNJob7nvro8YD0BT1eqJ2F3qdC4vXtJ3Lk5LCzfMXjcbZt2/aeKnvppZeYO9edAGPHjqW9vZ3Ozk5KS0uLfPPjb17ui47rD9wSMtsqpRi2cQzjk1UfSLteke/0ygLmn7JVBOI5E64AEr8FfRS8s2Ae6R0lCAFmdSsEwkxefhkmmg00inkhas6CvOjZOG0yCqFcnWqzAVNrDwHRzsDN59wi27CFwxei76AJ17S4LT2HyuhqdmdmhDZer74gW5rnpwuaJGUq5W+eblrPXOhR8MYhskQVwXYF+7TG2Mi69LvMjWdJZQroBftC4bbf0NrZlpqHsMazVKwhao9ldHwFpuokrQYQjbX1mKbUK+s6zeVR8EKOCiVkOUEezQo7FUJ7dxuPTA9I4gEVDCTO+EDykwpKaKaVqQznGFnJ03U7OX1GhufqDC6Uk0HLjbkXAjVbm8AynuFyLcMyKbjC0LhPbWS4PI4hlBYMJbKljYqud1np1HrWWgNyoWd+tjiXbndSZCCHMi0MNNYyKDOZSVqO8MUd13546GWPGrc3cVIWZzCE0cl+AAAgAElEQVSaOt515x5wNuOoMpflKEZ7mRf5ZfVFzpCjqGNDbq7H1ob87IALLimQO8D9GyTuBL0GXN9TWO+dBfNINRZHj7/X9n+c1+OPallFN+358+eHeKhbWloYN25cL9/oWfbt28ekSZP83wcPHszevXv7tGl/3MzL70XHpRBt6UYh2nHL1JBeX8paId/irMSJverlL4BBFjBv42pOzwGyyNOke0qvMhsY+OsOtGq3nAn31OeApXsTMMIFu9i25D9vHsTdow4EKCRdicW7I1nt24ehaqJEr9+FaLKIxR2wPRpdiZddIUhB+iDP85pmcGL2ND/+nuVs/O5crK0lfviJm0ozQ+qQHkL6FgoFcc1+AvuuIWyuuRbAb8+W2xagaqKMuel+4vu6kF1J/7k81K8lVbZPNR7IaHTacQ46M7BUrj0QRgnH4g4dPx/J5pprGXPT/YhNMhvmlGYre7kQhRaLsTd9mt9n+aE1vfVrT3pzDk1EoUJoZa9dCg1hRKFtb/deatvL0QxkB21Z0JbkHFWLQzVL2cTr7GD38jJuTexn24py1rGbyQwJtc0NgbIZpO/lSUdwUdZ3fVFEcZ/VzA6GFwwlWqU9z4KIhQvSs7jf2ci1WfT5tRyLQmfConpiI9rJ7Iyx+toM50QED6h1ONbYrE9YMeF39Wz87lxSW8uImBaP2m9zsxxQ0Gcc7LOmrnr22EP4urmfn6QHs47djAr0kJeyMn++e+Kjx2PRXtHjXh71zdGdtKQ7+f5v9vDDn0Z5+58FM05yffKxf8N9B/fGoKJ3IJqSoEViKJkMhWgK5Y191urQw9rSDT1+GPOsKPr9CJb1YdR5pMsqJkVDvlavXp1TFoLS0lImTOj5dN6bfPOb32TWrFn+bfvKK6/kRz/6EaNGjSqo/3EO+XqvZT13Vh1f/E6b7w/8n2+XccaKc4uW5SXPgN6TWgRlGc9wlbnb950uSg/1WcDyExQUSkrg3bQP3ZgL6YqMTrBrQSe/ub0CwzaYvG0Ca6LrSFk55K5E9/NbB2VxBi4tE9mbtoMeqFNm/azBWFtTO4jooW2eP97P/V1Ap5Acrp6XX3xbah6Vxsv8IQ3zzZw/+hELPmtqfpu89nhJJ/7itHJ5v9JQnZ7enSmH0bqg0VF8YWCWSS3QH54/PvTcBfq1kHh6HuOXZ0EIMoClzQHsth4nonX38UtHstwZyNdjbfwkU87Z+j5WZ8bwNh18gZO5nb9SYUb5pnYoS2OaG8/g2PwhrYgLxeWBUISfJSVJBrllp/pzTvQgJ0Xdw+TijBXSfTAjudSI+n3RnJrLmPtewvnBIe7dDJ/RLH8sHrYEnzWFuz0ZsLVLsCg9CFNLMMU5ijUi7DPOxzvkj3kho3L+/AnqBudwMMlNUIL9X2msJpIw0FMtvRja+zZnE7+FSK0bjhkMwcx/r3pap/L92oc7z96vzke5ziNd1vE7try/kK9HHnmEH//4x6HPFi5cyD333FO0kflSWVnJvn25Ru/Zs4fy8vJevpGTj6o540jXWbtyGs+dFaYQLVaWa45dzyuZCMcyLnd7zqzFlrXdOLQ9GaTv68YCRuCg11tav+AiUnJH8C8patEY/0IGHjva5WK2cshdRdZnnB4QIoxJSZslrGLsg5+l9IY91Ox6GtElMFTSpRpNzQmBWZrTc6k261x6zAB61ivR80t7OsH0i54/U+C4tKmB1rspMLuLykOce3U2p+Yw1Pir36b++sPdkN8q+/0g37hCsNxOUyagrv0QcwOI9ebUHEq0lyjT2jg3qrFISdo7OymNuOMoA2XtDpC/CBQirVAy+EQ9LPdp4fe/67NUvr6PZ8gopkcNdmdmBky9Ob35AprTMF8TYCvO1cA7Xn6VmWBBc1a/2qzHzAKXvLGsNus5zZnGYm2Zjyi/P+1QIobxaWbSnBbMF4pqvZ7mlOumSGoP0SGdEPp8e2oWlYbnEnEPN9vSc4jJZaFkI6YQpOUATHGQkjtA/+ZZVG7ey2An7KO2rNlssVy8w+7MDIYafyUqOkl3xTBUkub0HP+z4DyC3HsS5PX2xjyfb7tZFvZphzASlqAqugxTHOw2lN6v+ejxnm5hfmSHAZhuToBtqbCrpad1qptfOzB/epW+6B3Jsj6MOo9wWccXKabHTfsvf/kLf/7zn3n33Xf5x3/8R//zrq4uWltbizewgJx66qncdtttXHHFFbzzzjsMGTKkT6Zx+H/DPA5QbVSyqgiFaLCsfBP3MquFawPmw0Ic2p4ccMrpkLsD/NU5n56HbPbrTLigMIVGtVlPVDtEaIfPitBcE9xVX2rjx39ppUXrwLIcLpAnsyk1IFde3H2GtXI3y9mUpaI8jv897zE67TRz1WSmllYzXD7lLlgFxOO1Dt5G8n3HQYR2oRzA3W4QWSRxIbSrxw/t6bmnZhHqq/w+3e8MZVNXrv+XdU1hvDYEW9q0ajbzoxoPZHTWH7qQiBbJ5h0X3OEk+VKA3ewXGfh6SQV6qgWEe1u89uLvsGOQ+/58708vcPKhx4gkDHa0HtuNGzs0RjjE4rZPeuEB8oI4AM9sj61Cpt6we8EVz+wXNO0Gy/VjjmPujbAmVu9/d2yigVGHRvO4vYWhQjJYE5SLXZiqNRSX7bXhEj3BEruL+YbkSRuujIKhLQdgwv31bPim68+feH89z34NOl7NhZe1OYNpduZSZTZQgjunzqCWOrGxW45zhc629Dnd5tAIuST0GcB+7UmGqdPIqP6h9yQ/WiAoeizKss5XfOtYTxLkvg6aqINx055J3pNC+IkJso6EZqNkkqaOeaRaC/vbP8pr40e1ziNdVjHpcdO+8MILmT59Ol/96le56aab/M81TWPs2LE9fa1XOf7445k0aRJXXHEFQgi+853vvKdy/q9L7Yrp3BpfxnEFbtn5slJ7gasCm/Ttck+vt+egnClPDaGUg4keColHBgKK7ekzqZV/I05h2r2/vixoH72T7919gK+dNYo3MykmFbj0TdaG8prcwVSGu+kkrQxvsZPJWdDQ9vSZVJmFeaLBXRB13V1Me8o3nC/hnOE6MAilYNjGMUxKj+kR7RpcfHtCz/bWpx6v9ddkebdx8w5XXh1j9XByklGaZHdmOhVyZXZz7cf3/vRCuH24fVEMCV1lNhA3bRqT7kaTvzHmE5jkxw7n/+7p/barmoS2g9M4k7Qc0I2Qoye5smQrP00qiAgWZBnphkbrMUL5uHN1JrskSy2y9K2BCZshdM1c+KMYv5+b5Aotx8+OcNienkWpdMtbxx5axCEWqKkBIJs79/JR5kHx/mZLxW+kyb9rLvWuR5riHYA83WB6WYDVmbV+atkTtYndIh687wQJOfranwX96RnlmsQrXD+2hxH4OKXI/ERc6dU8XllZyaJFi0KfWZbFV77yFX75y1++pwq/+tWvvqfvfdjm8d6MGo60izAIuzp9rXOEHEhb/SmMKHDLzm9Xvom7SpN52Z96RsS6zGLj/WQM+WZ0BWge+lrmblrgLqIRS8NJhX2q6d1x4tVJTpkuOGV6K6Dxuaqj6GocGDCshp9hPlNZyrvsku3YSK5kqv+c1QE+7u7iclRH0q0QUaHP8xeiYH095QzvuGUKkhQeUUhPYypQWZOk8D/xJNSnjtunXmy5l1b0EdZyQDkckFCm5R+u3Fo3OVE6ZM4fu9VRbE4+yrdjur+gB32unm87167CC7H3XQ8l73GwA/xNq+dSMxD/20dCHZlK0WBJRkS2stlRbNTrGafOIv+t6a28obrgU9npd34E/mTBZ01Ccwbcg89LchDz5H4uN8NzL3kjeBZglQa9JUN5f1ja6rLcvatamR1z/feeW3ySNpRJ2jBAdUPZeyQ4lurn+uTT+3PzTF/Kb61BlBHnUo7jYbWOFBmutg5i6DLQtzme8ObUHNJqILZ0kGot5xiCP6u1aHYlI2OvuDgCNRCBy9Tm8bUTt3vdVr1+9QhqCrEC2l+AzpgGKRhBg8906LXJ6+HD4R7vK9+2Qym9HQz+nu5K7wn6smb3Ve9Il1VMivq0H3/8cX70ox/R1uYyuWiaxsknF78BHmn5sM0Z9t2B5B15EtmrYVf0ngkksleDf99ZlNbUq3NiemSf2pVvjm1zKlnCgT7fnk/UJrIShzPzQn8EDvbdQ7Gzzxzf18XmchdNPeam+zGb9/NOZD7pyODQ9xI1XdTyMAn2+59Zdw8jI3OHh8j1u4nuFf4z1PEu69nLDXd/irtu+gvK1Lmgcxx6LMqW/7mK2v/8s5/5KijVZj1mrJ1IbQJ7QxKUayLd+M25dDXmxYUH+qxQzvC3VwmuNpejxRM0HnRvoM7dQ5A1OSCIQKIMnTGf/z2JPS56vFBojxfSdFbpVJxkJhdbHsiI9Jo6yKDsuHiHqxXyda6Jt9BiTedi+zwesx/3E3iUaXCUJrgj5WCod5nFCdnQJdfnujtzMkONvxIvsWk8eBqWKiV4u8vdGl3RYjGq1XL/94YswUmDJZkb1XydvoDybMNgX9p2eduVZLejGCW7Hxx6Ky8m6IYFANjyywWM/NnT7G7cy32ZCp/YZI3awPeTKR/cCFmw1fckaAphgjYqxrzyJA+2Si7S4XoaGe3MJioERxOgn+1hPQhaK1wSlsFsS7omc9s6lwM8xzRGZcd0DPtJ4kTOIeMsyRLBuMvrtvQ5VJt1/lgErSznR2CR8zZnpc8J1bUtfY7rxiixqXC6053m92t9e2e38SNA+Z24AyKjXdfV+uvmkmrKpYjt6T0JSj7g0TEHo/qwhjrmYPgImce9dbwvazb0fW0/kmUVLaeYwn333ceSJUv48pe/zF133cWSJUsoK/t/L/1aPi1pUPSEgdYn9PiR5yHPN8fOlqfxhtzIUrGWiDimRxBaUOYaJ+DY3VmQzH/tHT0+Wb8fPa/4QglDxtx0P40b8ja3RC5N5vnaBM5nApZm8sUn5hO5fjd0un3V/rXf8tDYLlLHvoLMTlelFMMba6lBkbgTIqOh83NZRKyA6n4NNOrzyDj9/fSQ+RLMGf7si4ozGhUejZsXXlZ70yJarGlkpJvasdqsZ/vtFyKKv5/dfJVebLnnO53PDJbYT/jjViYHU2G8zXMOzDZXIkyHZ1NuFq/9tsIpgwNHwwEJ/WjkKRoZrAkGr9/JULv7+b0qL77di00eavw1uwEkfN2MlOxTuWQpGSnDZuci8sChFBcFbsn/a0GDeJVr+1wCPdKbetnhaiIa4+wMUZmlJZWjsLW3/A3bk2CaWICmlGS9VcF/WwaXMYEl5S9jJVLcoBennSwkLbKNOjZhEuUiJrKBfeyQbSjgXGqz1KQeh1/wdqr8edUdALoX4eTM1V4I3uaBK9gyuoyjmjLM7eXs1NP4Jf4HPywT8M3jR9+R66N1C+eR2lqcOjP/pn04fNtd9J4w5O8p3jrelzW7r3pHuiwu7r2coit6WVkZFRUVOI5DIpHg8ssvZ+HChZx33nlFG3kk5cM2j3+U6gzqFDJxn0iWvCIyjmKp//paZ2+mzWDQoOzKIIwwX7hEQ/VirvakEF/7ABVh4uUw89QD/uernlcMUR0MOi2FEC4qNohgT/wKtKuARjc95NbvTSPdrLFCvsWc2CSUpTFCDuRvb2ucNkOy7m3FDb/VSd4IWClGdq1gW+osFLrPm7wtPReFzuib/sRDlsVlZvHDUPAZQ/mneYN3OeCPG9YE9Og6zom6i+6W5JmMjL3K582DfC/lEBVwoQPW5fC5U3ML5TPPS/r/xxqiIoJCoNDp6sr9LJXu97vIcrILA9eNX5mCJkDBny38OOmgabrQmHsm2KDEpAyhtEuFROqtgHu49zjEe5pDCrg8Co/aufzgl0c0pNJDyOxLjDZ+lWr1aUn/xWgDtIJtAnde1PTXGKdniDpZqtOWUWgXv0XNSI11BcapN3FSFifGX2Z913AGMpAMDs20cpxIsgXJgchaauRAn1N8pLnSBy4KXD73bemzulnH0gpqY38B3Hk2zHiJ22cM4ZhrPs3M0RXsaNzLf/3+Bb7yt00F2/VAe7Lg+CVvJHTb1mpSfqiXJzIPBtJTXwSfBQ6Pb3uXOKlHMqDe6vwg9T7OZRVdeXRdZ+XKlQwbNozbbruNqqoqduzY0adGHkn5sM3jH6U683WCDFOezNamHNE6ezNtbliYM7fFax2q/30l8UAe3+3p2VgqbJ0pyHeeZUQTGTdhiB6LIpJns7b+EU4/Rfmm7Nfrypj1/DxS9y4jPqrDR6yH2rO1n/8zwKuRDdQY63k5lWN+G7loDj84UM8VpwuS/+TeRLR4jO3pU7BVAoFNM3NJq/5ZKkiHh6zR9BMbaVCCYiSUHoGGQnN9p7gUmh4JCAxlRSYN0d3MD5hLf+E8yxXp86kyG7hGzOAFbSmT0bi93gn1w7N1Oud3fYYtWRDT9vSZxEpsLGUgcNieOZM6p5HZ2lRUFqSlMrjukpYYKLfPejJN5495IRM6QCq7+XibUKdSpGUFRMLJLoySwvHE27Ix65pdwVKxiQGYtFhnklH9UYagRl/KikPjSGNyUKxngZjKvWzmr1aCLi3pt+lCNGRaR0QdhOZGMiR+nuRap40fz23nTbmLQZRw3pLZpC7OZXE7nPekOTmX05HUsREdjRoGMk1NZ0ZMITTF/tFZqlATGqxJHC2HIqISLIVSrik63zo23b6QLVL4QLSHjckcc804Rox2w2FHjK6Az55Kw5pm5ia7x8zHtMLj15N5HPBN5DIv6UdfXAVwmHzbRWhM/6+tx++3rGJS1Ab205/+lKFDh/KNb3yDPXv2UFdXx7e+9a2iBR9p+aiejD6MOgvpFAodeb91KnL39IK3pAyolGtum/rko0x98lHG3PwUZuUhnC7d/1dlNHRLjFCoTvu2SqzHRyKrIr5OldnAaS9InnvR1fFi18ff2UBsaAcqBc7WFCqF/+/oOxqIVefqs6XtAn+iAhFdi1DuDWGEHMi0lXFqKzT/QWUqRbXZwNj4o5haB9Zdw7Dqq7Hqq0mP1LFjjZwT1diXscj0RB0JPnCpylzGJrnVNy9WZcv2+uOa+B4GEEb81+opGiMPExWHUAgaHdd0HH0GnnvRbeizLyjM52xMcdAFLYl2qswGRqonsiFyio1aPTXGBl6R7/ht8h5Uqtx4eqZpIJz1KzDmngn2nKjGPkXo2a/Q3cQj4N6STQVT5Bh+kjL579QILuV4fp4awXcPxGmy3e95c0uqnBVmujaGJns8Z0biPpDLub0ca4TGYho5y3yD7yT2UxtfwfcTOzgxmgy3KSlZf+0c/8CmZIrkjbDqXMEmtZnNHOTAyO10ffcvxKpzh8rDfU88q8lsatlDJ+vYw/g73XfgqG89wdQnH2XS4w/zmL6WaNV+xt+5jHHjcmMe0SIIZwJLLYVmTSCq4YbGZcdtZ03K37A9GVFbwbsjCgNTU7Lw+CV+DqUPu/+0yvB74r2zwfekt77wxsP7p6f3h37v6Z+e3k8xa9//pfVYFfpnCJQpsKvc/3v7Z1cVdyMUvWkPHjwYTdPYvn073//+93EcB10v7ss40vJRPRl9GHX+PdrvxaF6ccwFs3xl38XgzbbPLHIF6ozesLubjnJ0aoiyst7ktBmH/Nh1GchCZlNOJD+5RcA+3x3486Yfu368lUAqm9gdrjnR3pP3nBncVRF4eGc7lzgWCLecByzBmT346jzg0pbOeSzmGb4ma4hogvgdOrFROtpCAVvdfj6gPUeH2umnyrQU7CXKhuSFTLp3Ff/fDxWPbBXElWBNveK0GYq36hX7rFLSDMoCnlyTuG1WoLoiZKTK+jnd+P2MjKPQKblDEBsF7I3R+Z00apvDFVF4JGCavjLqApSkWe7Hv+eb0B+wBJ813YtcLJFgSHsnT1uCfVJRKcdTqY1krNhITI72U2M6xjpqdM1n4fJiyS3Vz59vIpKLe2+RHdT9Ux0looTLOJpfZTaQzsQ4S41nSOkonrEe4JKo47fp3tsEs4RAZnSclI69p5wXW3fzx0wlEQwWMI6V2zdz99djpG8+mA3sOvz3xLOarJd7uEZzEeeStUh0Nu8awG+vhvggg0vSE3hgxza6rh3C1VEwAnNlemIqyzotZmsTUODHhAMMb4qzo3Gve8POyo4tezlqR2G+gvn9EjzUkQyNH0DyXyD+Kx1R4/5e7D3prS/+rwDRPuiyjghguYgU3bSfeOIJfvnLX2IYBk888QS33norEydO5NJLLy1a+Cfy8RaBG9MKhTMJqes++BjP7ekzAUXtinZujTf4seue2RuKHxR6Y37bnj6T7dcFOJVL8p7zn9swcW/mcV1SZoQZtjKyLBTP6yWeaJEd1CXfxUT3w7xS0ubsa6czVEv4SG6Bw9nmiSzJPBUiDEljcb/zPNGFs6gyG9ggW5kuoPEZ+MFPJdozguO0Tl6x30Zkx2h7ehaxiIOldB5yDnFJdu24IGKx2JLMFZJDNypsDbQ4bGs9089uVi4kSy3pxz43pebgiAo//j3fhO4+e6mPap4b1fhtVzVRaXCcNhmF4qJImt9kcj7oLya6IO32+Zr4QerG6QjxGu4ckrTKQwzc0skrba4Lw0PdlzM0u/HXYGtrGC7iaKKDhHBCbYq8LUg5ZWz8/JkITXFwWBtP7ixBI8WpWZR3h0zzCtuZMdWg8eHDmoohCcbcRzXF/8/em8fJVZX5/+9zb9Wtqk46a4dOSHcnnQRIWIKBJISEQCBNAB0dRYOIRMBRnPHnNj8dZJwRwe3rOONPv4KMOjOOIoIOCCoKJN1ZWMISwpbFhEC27uxrp7e6devec35/3Lq37q26tXTSCYnD83rlla7up56z3nPuec7zfD6b/maeP38m2s+S3D3RjSrPWhykB+FMxT2P5dMR52vnQAGGvEJnZnc9D/1sLXx8KmOb6/jPW/6LhqZRfDHCNQ7ueNUJQuPnyRufuNwHwkkOisa+DuLY9yfA7FQMRDveUilgWdjZsl2hT6g59kC0Bx98kN///vfceuutAHz5y19m0aJFJ3zTPlldI29HmSem/oqGxHIS4rD7Mp5jL/LEhdK8suiueqDb2ZgLgIllWgpy1/MbbThnGgqfitLIb667Oi568CFKC9rpl6E0VloO3RLfjqXAEF3UGy+xx5pJ2hnkL+T12mAa1RDqRA0TRZ6taj1bmJ7Yn8v/zSF+iR7eEg5PZJW/6BrACH0vE+K/Z6ltc4kuWJJVvFsTvLVU0eUI/iqp8WuxjgZ9A4bmYmULpSGT8HI2vKENEg4N8WWAQCm3zxqM/OeWuBvQ9cFYDKmEmwVuZiHutr1PqdC9dUYJ4qIPqXSsdJy4go8nd+T6fivtmRZ+a2Q4fM5GJmpZtsg49wqboRqctbmVKRkYe4Pi0jn5DWbFs5IRdwqej3vkNXqOVesN9qguN/gsfgShLcOWtUUR5xkFcdHNhH9fRXJcF1bHYLZ8XqJ1nsMujrBbdpPF4WYuQForfLjXSnPRexFzzCx7ZVcu5z7uv4w56jAfi+9nXEwhlMb1hs095mH2qDAzV/CZCc7toHiwp7evOszS9ev5hemwIGOxdesBSEZvktI0/fErDMgL5WxHPMOFz4otBxNLVubJ7o/eLjGraJ0orH81m/pA6g2cLdGvPG37nvqSJ3EAe29lwpCKm7ZhGKRS+ST6ZDJJPF75snyg5WR1jbwdZZ6I+jclloQJQwqCkjQhaUqG02s6zBacZN1Rl6kMgcgqfz/Wk3GE46AJB4EK5a43JpaGCEN+09XDwrgWmTcdDPz5kw0L1FSUKG5jVDuD7f1I0I3seBCaS9huS37mvEGNyHKd8hbyQ9w0+Ai/75nkbjjC4Ux9LWiCZx04A0VjYimG1oWWrGGB6uOracltCXeB8O4mbWVzMJfO04nkUh2W9qT4SspdTFxXtcZ81cJo43lSSRuZzkSnUAk3X1sYEJuQxHojg1B54ouFcQ3pI3LlgmIc93ujhxo82mPxsRy62PVxLYfb7pBMZZFpF+7FI8doSrYxJ5blnBvg0jk6LiWkxlPPSob/H0VzQuP+VhUKrFu7RHEzOnWxLL9Vj/CxRIxVWcnh7FCfPGS1FMzQBVoyyfUyHHF+Qxx2WwrzCw62AzKR5IOZ3XyVnXRh08wINrEfDTjPcBA5CtlSz8lGuY9J1PkvYomaJNOdVX70uJdzb2kHGB9X/vxZ1dVLJzu5PXmI75gj+E1G8JFU/pnxnhNvbgelKdnm9+Gsrj52S8E1hos/32M7DI4Vb9zenI2KoC83tzUh/SumoA5m8WZcSJDijnn5DUaiu3gAFIO9hPROYcKQLT9chJOqqzpPW1XQ07VjgDH1ZNiwYTz66KNkMhnWr1/P448/zogRIyoafkf+8sUj5QjKsUQ72PfUo3/rYAiAxgWpiCg70+IzfS1JZzhoQxuSMyJ0Y1oMTZzLk9nXGEqKuKbjBqjrEdhppcV1Q+bcyPE4Bg4SnaYYNGUGMYJhgYX8IDtVisPs4svJI3wj3UifE+P2hOLXlmS8VD7pSSrnnvxqQhTdLT+QFXwwtyG8R3fTec6PhyFOU0JhySEodCyVYqc5l2tjK/mT3cX1Bjxmx7lWT9FhzqbeeIkR9x4mNgG23jCf03a9EHpx6TDnk1WDUWj+WH53xnlc8PHzES9s5omfPcPrTWO5cKtLh9mYaCOmDHT6fPKUuNZLh9nCy/OWYq6wQhvzhvWKW39roIBLPiF55nnFpbMFT61UXPac8NuUEILN6WsZCdyeXMwe60puEENp0hfTYc5yXfc8Rp3oLHILJ3+gs/0bLWT31bE7+whJ0c2dyUN8PR3nGs7iHG000toJqvTI75RHeIi1TGJk/nqjz+YqeRFzGRTKub81FnZbz4wLZsZdNMCvJA/xqfQIFspDxDTNJ0sJPidBJrAdmfyVxQ+z8Nkg/nwG/qlypmG/pPAZLgXPW4jRb6taNKLd9XnbLTiirmSUuQcnrMVAOS5IkzCbuZ0AACAASURBVFKKMZsmMrmvsUi/VN2ORm/AbH2yl3hN9uTK077rrrv4wQ9+QG9vL//8z//MhRdeyDe/+c2KFRxoObHu5VyE7dvgjildt3Lu4EplerrRTpxSkJzFOmEpBFyorm6lXZLxW3cDsFHuZ7I2Cmma7JDzfOjUwrJ2ZOZRH1/CPruLl+RIhjgHGS8liDD4pcBmtjqNJ7JnMV87BwsdFz99XhGecyXoTt8NKVyPgMcw1SJO4w/qALtVj8sB7cwhmenj9uQL7LWuoEFbw40Jh2Aw3ALm4EGKQvClQLHN0TAMSAmo1fKbWVJIOp1hdMuDBe5+tz2G3YOlal1bilyE8mSMVLvfZ4PNNnijFxF3An2VFw+IxTKHsXyo4IKPn8/Y5lGMbR7FU10ZLr/ybF763JvM6D7dL7M+9oI/H3aY83jO6WB4q4WlKZ6ZB5fOETy9UjH7IcGGX84jo4bTlFjCs0s6uWSWYuVixS1SgOZ6CDqdPNKe+3IlQuPvue6j3MJv/I3LAS80k+nxOHMS7gb6SX2yv0EFYyJa+1ZzBW72RRBy9gbexXNsYyXbGcdQpIJ6bTDr5d5Qzv0rjsOcWHj+bLdlCMHt+6YLc3ql6mWkNsTVyx2qvDmU0LpD7uzxugjjz+vRp7T+wM1GS+W1xYtt8cSwerCc8mRPCh1RZj2oNQeVhBOOWqtOTvd4/225qa3R+k79MbjHN27cyOTJkxkyZAh33HEHhw4deltP2CfSvey5LU+0O6ace9mrU7W2PL0gY9VBbTEzE8Wu3yC3chQrFES7jZuSLgxm0MVWrm4h3t6IMWisXYYmOvlm5zB+FHdIDk760cuNyaUhO/XGKvbKg9yZqWOUaGAhU3hdbmC1epXPGYcQ9oIA9/ZSUoNsPpnOINjkR8JGcSJXA925MK75el4f7ImvCLlxD8XXMb32NHSzm4bkCkZY4dPxSH0PE4w/IHB89itwN6F/7HW4zBC0ZSGjCN2jmwpuSnTyu8CJ/OOJ/Rjao3ic1XY6g8ChRdP4TVZx3aCNCBwak0sROKz6kmTDJA1R2wrnuG1SwJQtcAF5Bi49GeetxiFcHohivuxTbtT9W81LmPTmc/71RLAvRhutCBsfLGZdLuJ9fZtgEQICYzlnlRtY9+7nXbjWRbk2Xaku9A/CAse/ihG5CMLl2hpuCoyHP4dytoP83eUkSGsbDH7zIGe7ZYbX2EUvWd6TOpcG9SSGNZNzlBs1fjPTaYovBtKh+VOE4KZcetqZSZfGVSbr0E33eW4K9EdQtjnhOIJtTvRLd7Vws1F6QcY1cMfSmz9ueGD0s9If3nk7HX0inwg80KqYO5s8nHCr4sbEikj9k9E9XrUtQ+A0j0bfVjpQDcAZOrrs36HMpv3tb3+b++67z//8hS98IfT5L1k8IIG3xR1ToU79rpfyKDwlP5UJhtoLiBVAVFZrr5TsyAQiVCOizKEym9Be2c0vhoxCdQ/jI4znXzJbIKtzk97NJKOvyI3nsX8NFxZnMtlfGLNiLXb2/UUpKt536uMvsjd7ERk5zL/T9hC7jkXGOK77tiMDNwjAhvaMyz62NzuTQ/J1uuXewOl4JFvSbhtSARq2PtthNyNp0Q/xsA3X6oo/2iJ0j77buhLl7MyhqU2lw3FBdTxX9U5zLqON5/mD08ks52o22y6vuAdjOtzs5OyFMhQE9tSzkuF3Bl/A3LE6o6M7Mv2obtsYLNkbyaxWmB72sxXwzZTk2pdTuDl0eTnnBxqHviFpz8IRqXgiqxhGgrimYQX2qCA/+otyM036WyyzDa4xQuZCeqmCyOxCKaS1dYPfYj7k7K5c4NoNTOUB1vBney/KrgSpk5eFxhHuCSC4fcY4Qod5tUvhKSoz030uhxLn4c9//gSFEgVd9N6zXS3DWJREXaN50rD0MM9c6cIJP7USmpYtYHMJoqQTvR4rQ6CPrsHZUv7lpBod+4dj0CbU4MgK3BNanAvLapTZtJVSZT+faDmVoq89ZC8ALIVQb1/9oximTGlzFWdQr9VWba+cC85z6SnwWYnAhcyUloZQkvZMOECssMx6rZYxZ46kfplwKTplhgOyj8bE/sgyPeakBuGwQ+UXxrGik0mpR/2ANI/FygNN8erbbs4HVAixK6ZpA+BqzItjZlFxFwr1Fil5xI75aV0tajoNyeXERR/PdQ1iDJL7sqMwlcH1OXdqHxn2sZ+z47l0HgRGzlUehYLXkWmBrAClWGanqRXwotzMNHGB72JW6IxXGr9qlaG75vWtikXobDfns8bJMFkbAmaWyxzBD372OuRc5Du37GftL1byoe4JqMHbUE5xXxSmh51mOTz+5GA+q6eL3tv6vgDnZeARBZ9J6nwtrfhSPMGhEvE6lpTYsa2chmATGSxJEU56cMyjRGCj0EvSo0ZBzt6sTQcpEGTLvoAGy1ztCDrZ5QekrXYE9YG5QYVNeHBMp9t0eCIr6ZGKwUbp6PFqpBq94HMCxTSsA12mR95zycVdPgZDlCgD7HpgV2U3dDV6dqOAbeV1sj8dTXZ8gkrgMNmCq5tSUg2D1zGxfIkCn3vh5xMtx8M9HnUnG5RkKjqvsZKefc8YP6w/futuxPZsVbYUOnrSQPalKeTfDda10FaQySnYnmTKob53cBHD1EF6GK2l8HJEPXvlFqNqXHAdmRa0VA1mX65u6fDfgxy+erI4t1ru384uhrIvF20d1/ZQanzcQLRWztSO8D/OLr6bPMRt5giuy21qRdzQybA7b5fs4+dmkiSDQ3eON9f20uhUXpAK+0MIyTLnVa7Q3oXXh3oyzo6+y/02iByRC9nzUNpQWtNTOYPh/JRnuTtRy5mahe6E3alKE+69rdT58KAEMt3nE09coU0N5Yh7ZWZ6+jisO9wQ13hAbcXJnuffSXr3+HNfzvDMcxmXMGWl4pKVuVgDZeeipUeSqEmxo+9yPvS0YvPa3/F0Y4ozdh6m6UgaIcaz9XvXM+HffkPqQDrUF17k+iEJP7fqsFSCjzpT+L7+BqYsYOb6Adz3GfhAbpi/lFA8lO2lJcQJrfzxfNjp5f26zZcyI/m3xEEezIG8BMXNLCge88I58YpFUf6+cBzO1eo4FxeR7Bbelfu2TTLlMMp2ubK9YD2P3CNqXgQD0u6o6QQE7dKtQzLlQATRS6F8Zngtdx7u5o4S6V6FZZaTavQG0panJ8wIfu+ATHn+Ar753RVcsGJGKGc8KMKC5L4MZqaK9XifSffdDWVTq1IH0qTrRpb8O7hPVGrHEdJ1KZQRAy16bdS1WMUTdLV6ehUETwMci3hySujkC6hxcdSWLI3JFUV5kkFxxle+g4jUu61AYVh1ttozLTjjBtOw6wkM0UV7pgVLuveyTVobCdy6aokaZABr29UrdmFpiRq29l3iu/v2ynDeaFBPymiayf5IU6KtqG5Rst1cgMOoot+nN+7EpNc/maTUIRIFpygvLazRcNua1DT+KtHNsiy8N36I2bn7zaZEG69kIVEiC2ZWzfNsN2uJy/NCm+Q4Q0Omo79TTlzIUHg1q5gmLvB/77FtAcTNBayUb3DDoPU82zuEh3iDySS4jqnca23AJINiL/tkN7ZwuCnWyb/n1rAPD8rvTE2JtjB7l7gYKzMEoRwM0c2T2gssitm4J0ibh9Tv+XgivEE0U8MzrSaXXAzrWxWX2oJvWCNJsIHrmMqjrCHdp7hJ380ko4+ubrjqzSM+/vgmbQktn8+dwlI1Idt5hDUN3bK4UExmoohm5ur7NCQtQqA1NcKhyQif8JoSbWy3JeutOvY7CRYyhf+0NrCHDNvtA0VsX1HSkFju/6yAtFJ0SxUK6GtILA9FSXsicNADc7shsZy91kzqjVVl+N6LxTu5VvOceFJuwz4VpNRp3ZMzEzXEVihmBMYHQBqx0Lrdn/X49Nser6jTn7V9892LsJrKb/InSkpu2q+++irz5s3zPx88eJB58+ahlOtSW7FixQmoXl6iXLhRJ+UocvbgyRdA29GF3TCGLSwqW6axowuroTQ7TX/0+mNrS0NxvdRnY6gO92fHLKbR9PUCv/f0PHff7Yn9/H9WjJccwUytWK+cHIs7LOo84ZhZ/5TonVYGnd3I323ajiE0/j55iEeywk/J8sbauWcU+rcOwx7yru1YJ4/iBiR5ZXl/+3EZt3fUnaM0q3uPjcLlvt5wT7Z2dipxTbi4yzHl10ngMFMYfKNvKBr7QtHJ49Vo+sRbHBIHGaJtYoscyb2W4DxduQQdIl9eEK9boZO9ezRL/98Xudnai5CCEfrh0Amy0wlzaYtc/Wc/6waBvW+liKC+tDig+miM7Seb68sfioMcUMKngMzkKCAd0/LjAgTh1LiL9CNscMJ9XEh5EAWSEo7/d2VcTGOwZTEpEMPQR/glwJsniuK5uMOcFwhObGOuM53HxNMh7vkdmWhXZ2OiDcPMv815MSZe2p630adtpyz+l2fZe+YKSyv8Xrnn7pWkZMMEEKLHN+IHFJrFLzHVuserkWr0FPn+KCeOaTFdaUW9vvXfricT2CirXUPFnh7U6PKR7ca+bqzTwoAv0afpyq7valza1epVoyNUicvqSkxeY8eOrWj8WCWTybBu3To2//VPUUfM6tzjVWBfV42PHdAr50ovBQ/oiUJHr0mUtBV0S5eqW8g9PqiyOzvKVlQbCuvmSVNicRhcpR/RooV6niegUjvNe0ew9vb/4oaszQOWxizj3YyVT5HQukNegL2ym2XiZZLEmKqmsDbgdt1tzWEJb6GrIVzIOF5mB444zC213UVu71VZxZPZ4f7J/ur4YWYNGdTvdt6XkXwgno/w/mVmNFdyGXqN4bN8Ba85lsktjGQwU7UxvCZ38Rq7qBc1fCa5Fk3TeCg9gdrYW6xzFF9K6vzaknyotgZD05DpPtozLWTkMN/mavlnhhvrOGydw9zaSfyh9wUWJVwEuMO2w+czdXyFS4lpWv66IFXDts7ZvBrv4mo2kNC6kVJyT/p8dDRsJBcajSy2NpIgxnQaeJpNJEQfNxsHGKHBLzOjWcAlxFIWX+99gX/gUs777zaS4915c/9/ShqeEjy+M9fH2Tre/4WDzH6PW+++T0Pyduj6tuThLbmXHkvjvVqKw87FfjBUaI7YDj/OnIchdCzl8LeJtSQDgCPbzQVk1WA3wG+Q44/RQ1lJbeJdtJ+xO3fVJwFBp+xh+JYeBh85nwsD8QH5zT/Pb13qufvaR2axa8QgACZYR/jqH39Ac+fuyHnjPQvJQa6rPaF1c/O1X2PbsDH89yN3FX2v3HO3FUnnXUQGFDZHcEFV8wwfy3NeKDdf+zW0mhpu/u06Tj9UGj2t0vpZqHfHR+awa0TpTXmCdYQtRthj+NXLtzCmNh/F7rrHU4VfLZJq9Ppjq+/02pJudnAD0a678J9Zt24d5557LolEokin5KZ9MsjJtGkXggsEpVLIf4fZgpOqK2krlApVom7B71STohVlK6oNhXUrpTsQKRKV2tmYaONp55C7RKokVw4Z5tsqbOsqsZyUPIvzxOmsVbuwtbV8KNkLwEPmIOLOVM7Txvh/u3ZYCs08kDvd5xeIY0llkek+JDoPWVk+bOQXyQctjenOB9FrDE53HmevNZM/OrtZoJ3lRnjTyb3pcxFoZLG5degevtflcFX8CDNz7v070hZfTgj/ReARqXFLbb5Mrz+WyNcYHt/CDYbDry3J+wbXs6vzXbTyJAc4jYwyuIQprBUbMcmwyDhEU0yLHKdVWckfrUY+yoXcz2tcZDTT7awh5UzmPG0Mr8udSH0t1yXdjfTf+2A/p5HUEkx1zuAV2rFFFx8z9tMUy2Ori0CflUr/acvaLsFobDAtojRox6qs5OHsUObpB1nhjGRh/DAXxd0SgkQkXsqmbu6lNauQKLY7jcz8lx1FecHLvjqO91sX+WAfQghS4gBCOKSdEZy7NcNMK1PyuYt6NitJOb3CPis3H++/RHLh+xRr1gsQii2bIZ5xOe7Hb4SF3fm5HmWrEBGtUv295+dkTb96O8rsj61t372GTFPp1OnYfo2F1971l7Fp23u787RS5UQTlfWq0RlovQGz5fL2erjJZZP6j6nM8OdwmeVsaVCSsjLw/TJ1WybXuFSjmiiwlf++lHlOYxvJ1ZyBprkn2qi/6TGBkoKmxBIXOCS36BZBmQoNVGVIQk9vu7mAP6nX/JNt8KTttdGUNv/CCr7MPJJajPVyDy+xw49Ormcw12hnhcy/rD/C9Ua+Hr+xJB9OxPwyLTWE1XIDO/U1fMrIb+73Z0bTwqWslhvoEO1MUFM5XzudNXI3B+njcm1CrvrlxtP9/TLWME+ew2LeREfDwubjiTUMjel+O2NyLCMZxFRtDH+SG6khni8DJxxnITS2p/MbXuEL1DK5hiti7yozf/KyjNe5gvND4+f1iz9PNMFo/XEek33cYEgesHTS81J8/q5uP2r+B3fUctmKd4NU7NQOM/Q7rRFgH1e6mPeh+Rjsu/w8ru45ERFzOy+hOVlhPm5FsuHTitPHCOZeHMBxXylJf01xSXBTjrCV77Pq6t+YWMoeaya2GFzVepC3d7zWqaPUO0lsFX5LJDU+eOhnZTftUyYQ7VTD7j5etpoSS0gNsiPdpANZZhH2eBUn0O3mApzkKMY6xfmnhUFupbDHEXCFNRWUq+PZKvx+8J7++47BEW0No+WVZOSwopSdA7EnmDtM+PUvBJQISn+jZ8cll/AJKXk4l87l3Y2iwZ/ZR6t8g0EY3Mg07ucVeqXF9eIwt2qXYqkYX4nv53NWJ59OvB7Kn38+I8OgKpoWKvOtvhZUfANNhFGzRsT2YDhdXJvq4HfZTnY5h1kndzJC1OQCEF9x619iPL1+9kBH2rJvsk818OXEIb6TGcFPLMFtMZc/+wo5m3GJVv7TGs9ueZhX2M3/M2w/q85Y7c/IVeTvWaeLGsap0n1vmAtwAmNeTs5K1SDTb4Z+Ny65JDRP9GSch83eHH2nm9b1o+W1PHMVfl7wpBWz/LnopR/Nnd3lb+qvLx7E3Fwakjcf3Ze+/FwMPiuVnhOvfnqV7aw0H5vRWLlW0dktuWRWPoXvuWcVn43AFa8GXKXS/B+fXNwvj9TWw5eUDXDtzzq1vmcHk7XTKuqdKlwQhVSeA0LNeTKIwCEuugeMMu540M+duDLz72ZNiTb/jmygygze4fVXhJAYojvyuyKQwhMFfALg3DMKNS7mp8kZIhNpa5lcw3ztHM6lDkNr485aE5kWgNsf51LHOdQDgluYRmO8FagMD3i0YmgacWc8T2Y3o2WnENNiCBwuTA1hdXeS8xmbyz23WMMuZiR7eDXdy594i0HWUBaqs/yUMy8dqpDj+qO1yVCZS8XL3BCzeTRbQAwiNDclDEWzprHB3kEHGqhDrC4IQIwSISSOY/mgI79WNlc7kzC0l/hU7EzWi908mc1gOBNpTi2lLdPFJrWfBqFzg3oXj3a/xnUfPshlgdOqd89KhWu/cvOnvyJwI+nbpU2t5s63Wk1jnOji1TJ5wROXzeCZBUv9VLjJT5/rpyGVmo/9qlcuXW8gZe7qBL/Ipnn6ecVlswVPPaeY1jqgRRyTCFE+5avadUpTaR5lPbfJYUUAUf21d7Ks7catO0Kfk4OcY8cePxmkKdGKLXsGjDLueNDPHe8yBY4PVysDkaFeOsVA0ex51IDu4pmXaqJFmxJtYITd4xINlKDByKdztGdayEZEtzZ/9oF8Cl7SrVe8oB7eCXCT2sbVcXch+FWPxvU6KKX55bRnWsiqIW4Kj+gO9Rm4/eXTcfaznVF6H0xu46EszNLHklVuSpAmJV81OvmRNYjdsgsbyVeNN4lrOmP0QUymi5Q8M5xylrsLDpGTCIhZFl6vSqUxTHexx6+Py9Dm/sFYLTX6cjrsLIuzo6gRBgvVFNaKDSzOxqkX7kuBNE1/znhzS+b67z5sPhhTeDjpv1VL+ZiI0WAspwF4KCtYmNwGwDVJg66MQ0KekYs6n8zzS57j0gjglmCZ4LnH858bjOVk5VB3vEroeT9b6TgxpYcyEBReVL1LK7si08PMmOCPlsNfGbqf1nX2svNy3OwXkQdhccu6KP4qD7cqLrkY1rXCjbGXIfYykHtORF8RDnq188frX2/+q7iOVAGY0AiHWTXzcZxpc81qwXNDFXNnRZ+yq6kbCL9fC6WwflVFoit3nILPfpRUWqfabckvsyMw+pJcx1QeEy9hqgyL4of82In+2KtW5+2wZcWGVbRzSmzampBVUcFBdZRxA2nr7Sgzys2lieJ7r/6W2Z5pwVY1eSKNwIZZjdu45l7QUkkY5erd/5+SGz/hRgnL9mBdHVIpsyhidOvdH0GNizPxs/eT6DjoUz5C8AS4wT0BxtJsG97C1pnPMPwPGZ5eBJc/5UYjm98B7U0HoWwENkJE0xJGydGCS+ywruD98RfZk8PedCkrbX6Rho3s49vxbr6SreVX2QQ3x22EkFyrp/kPq3Q6VJAMQ0u6J22Z7uM1W+ZyjN0T9mAcH/5zyCAHmXYYF9c40ynGvvZeCrRkEgL1d9OHXGLOg1LxmCZQSKZsgVSXClFIftgQBD0XHxnayw8PH/Y5r9+z0uGZ54RPEuIBtxSW6fZTfhzac+A8XlR1KT1BeD3w/iaQaMKhyViM4AiduBSiv7QklpS+S785sZRBKxQzEi7md6Grd04uFe6vV4pQu70yvTKiYD3LzR9vznnzX9h2ETVnf+wFdWak+8gsdvi6klxU4pRdTd2qXTOqqleV63YlnfFxcnPZS/Ubj62tzdGhFn/vRK/Hx2Jr8w9vDKW1pQ5UBok4JTZtiVYVFRxURxk3kLZORJlRxBaVRKL3u8wg+IQ4Cndy36dBS4FMw5KsIgk88pCiJR6eZg2J5UijDk0WRFx+3v3v1SxMi7v11+lD5L4TOgFa8Jv2VgbvzOUN/5ekLyawP635ZXgSdSYK0iEOhDyvtbJQi+Gd2DyazI+mMnyUgwD8u3HQL7shsdzlimYX/zTc5NuHk5Hu60KOZC///CIO8ifhnrB7ELxiGXw9NRhLGWi4hA9uHvqRcB46cX9sg5HDw0zJ2R8uTh8y7lSklRYijii83njBSnCYXfxjLnXuYFZjU2uOJCR3yg5KKVv5edEXqlt/xskb9/syeUrT98fg/1qK8XIi41NPEdf6uNCO+68Bhc/JOHQuXmEzHhE5T7wySpFpFIpnw2vrQLQzSi5BZ/tTTjj4rIIURo+XWjOqqVuhrXL2+qvzQaOHe80j/ovhp42eyPIGsswTYav5cw+GPstkHXzg+2XtnBKb9nbzKpwjR6qLVsxUjt6sRqcaPW/xq4amrhqdUnr9pY8EF0gCS8NyBodYrQpFoUe2szAQbbXqZcPZKvTgRgE5SNPMgY3EchG7Gpt6/ypA7p6zkImIpDQEtpL8yFrBP2QvI56BxngrCa2LDnM+Sa2NWs1dams1jf1C8uHcDH5PDP47U0cLl+XLwIVObUy0kTB7Q2V53M+FgUBe3/rAFSXaGxyDtqxkKILf9E5muuZG4no44EIWYlUrfwF3uaKXELMs7qgpT++3taeP+6yRJNRQFjKFlWzEsk0cZz+jY4Jpccenycw4Q2lKLOF15wj7VDdfThzi+1aM1Y5gtJWjgTR7/dQlL8p7xRKbubP7Qm7tuCW4xLmSzWnXbVcYfQ9wmqb4cnIZcZGH6xwZOK0W9q3HKR1li4wW6LOICPQCW4Xi2U7qj4TmyhhN8S51Ph2Z/DiQKyE8F93/R+JgJdrYY13scoSLHqx0nLiCjsz8gkC0PKaBV6/g9Ys310IBbAXz35uncdEburapFhAlP18FD+ReRwqfzyhbXn/59Six5hWuB1G2PPCaUEplporo8Yi1oFDWyz3sDWRc/DF9CedoJRixqrBXlc7bZOuC8hqnxqY9LrkYOdQ+KXP0jrXMo8kTrsY11ZRcGsoljioL3LfjqLoVbvAjRYKzP2xGAjmEbCXqeODIHj4Yz8No/lb9jo8lYmWpOffKbp4ctZWEiHFd+1Qe40UyKsvHZCfjckEnh5w6umU+tapJC2NHD9MPghNuYUNiBUYBDrVE92kqS/XtcJPIk6fXXk8vj4YmeEBt8JmivDae7rSy15oZogk1tK5QPrFXt3IvCtPFYM7qkcQyzbmgtvFktDUYumCmDr/M1NGQWEGsxvDz0WdqgunxnQgc7oi5o0o85xJO1kA6Xx+Bw2UrFc88l4+sFk/DMBJMqMl7LV7NOkwroJJ086EPh+o9EcGcFSIE8uH1WZA+s5By1UnW+X3m9U+5cSqUccklSHRWZpwQRKmjwBBd/qbZmFjqj0twLgb7Q4A/TxQ6WjKJMnuI2oC807Tu1UuIHElLkFY2/73C+d+QWEFc6wPy3wOQiTqfnraUaMkkw9M9Zeerp1eYJ98YMZal1rIQBW+iWK8huaL4OwX0wFFSTfT1OdpoptY04fRZ3Mz0srqnUvR4lE4lOSU27e3m1aiMeeJpMk8AFVwhiEmhC6Val1k515S3WZYEVxF1RXUrRERrJsYzrUQyQ+VtzccRo0iKB0OnnITQ2JwuHxJZr9UydvIoTltOjthkPIdFhtFaGnKY65fLOTxm/8GHnex0RhTQXRangngwkylsf2EN0jcmtK4iF6hEZxw6K1uzRe39KAYqsIk8kM27Yd8bs32mqGD5hfUJ/uzRaWpkSrqovcjr62rS/DDTyW7Zi41kJL0sjGs8YOlcLufQkYmx3FnL5falfts6zPnUGy+R0A7zP1nFwngstAl69WlKLCaencmrS17gkou7eXVxLVI2cHlsFx3mLNY4GSZRx70s4yvZC5EiH3Xt0Uyur+lkwwTIB5DBpty9eNAbE+z/wvQpHZcackPNTnafGUOI4aTEQTThlIXoDPWxOZ9ZTg1/FH/gIzlWNZfSNHocgrIjczmg/NN9kM5V02ow03rR9QDAw8ZU9o07wNn7J4nVhgAAIABJREFUDnNFOkxX62VkBKFki+ocqE8I/VBzGCWL7/gLpRmN+0swtxW2r1S2iVdekFq3VDZJKQreoCh0dJHw6YHfkYGRU2LTFjhomYNFmOJREoU9fjQ6ALFMJ5LKLu1q9ErpeOxLnhS6x6t1DQZxlaNsCeyisny9zL6IuoUfUmmazHkWnnlOFQUYeSJwiGX2cdgZGdpMO51RFLL3eGMQXBQuXXgubU89xx6nGxvJ+9QYvx4Ch5img5rMk9l1aNkpzGcij9mPcYMh+bEZ473qYoQWTCsTBGkad5iXU2+sym3Qtm97h5lzGWvdrO4yqXMud19ylnbyzJXL/JPnzJWD2GHO8d3LACkhqM3BEnpMUThuffUq5qzn0racwTQm2niu9UjkwitNk1WWpJNdfFRM42dqG6bTwJPZdrTsZOIa/E6u4ly2sVpqNAX6bUdmHm/qf2SosPhN71lcm+ogYfYG+sHtsaZEG5OXz+CbqVVcsGIG40UC2EmHc4SHeJNJDGch0/iN2kZabeYqJjFaG0ws0wkxFfBO5IF/gqc9b94G+z9YB4WOY2ZpSrSxL3MWDTfsC9jSIm0FJY9/7tCcWsoOx+HJrGKUcKPxw+3NS/DZLHzegnSuKA1SkvYCl/I9F4/mvJunck7zKHZu3c/3fr6SL76Y5wNoN+dTb6wKeVzKrRmhOigNtPJgM15fVHo+C2k3IyXXxsK6F700FOhFSbs5HzJaxbWx2vV4IPVOZluV5JTYtAH0pEG2d2D0qtFpTLSFsIvLyYnG9I1yDTYVuLmOR5nN6T7ub5UlA4w8l/zf9PaE0pD+JrGX3VYvWTUYD8/ZSYxE5VxF3sll45u72NOc4cbM+Tyw7SXW6janBwK7QDBDns0yO88jPRydP2ayrFfD+PvUo+zKLqA+/iJ7rItzLFh5msYmtTSyr7zPtpT8KDucnyRb2Wldzaz4yzyUS//5c6tkESYk8+5lme4jo8IgKN5p32sjBTSr3gl4u7nAv1MP9n+phVdLJpmVhJnpTmA538rV/aGs4vpB69nYW886jvAFXfFwfF2u1zSakkuxpGS17UZS/1q5f9OSg8EUPluaJ7ONl4ivUJym4uyMxfiFmUTnQJjchCHU6puZk9zq2srVvxmNHy9xSp72vHkb7P/gzx44D45bj/tbFXNnU9aWJ0FedM/mgkGD+E1XTz6YTxS3F/rxnOTKbEiu8D0zrTUJzrv5esY2u6x1Y5tHwU1zaFvbQUtfJtTGpuRStpsLqDdeIlZjVHR7R7WznI532i71fB4NNWfUulKtreAVnSdRuBIDubZXq3cy26okp8Sm3ZRsRZp2cbRxhERGJR+FDoCl6vodFVjozvbc1sFI6JNJSkWZl3LLz4kIMCq0FdMyjBA2T2YlI4XI3fEKn/aww2zBoRieb8p545n8vjOJ3bSLm7Xp6IaBcnQKQzeu0KYC7j34S2o4FjGuZzLfNzdi8iqL9B5ErvbfznTxtVS+jQKHV7I607yrI0PQnnb4ZWYECdyc5u+bG+lWm1hkKy5+VvCt70reuzIcQe2197q44He2w/UGITQ0T4Jt9oJ0ZG4D934O9v84dJYvSXDJxb2RC69XriczhOAbfSNIs4nruYDvmxvoI8OP7f18psbVeyCrfBf+e2LwYBZuIRwF7Y23ROdCW6cDwejYUCZmFSlnDBO1kXTLDK+xk16R4Qp5ERm5GkPr8utvSYl6KvjSAXMKT3sF0dJhV7MI6V38rCp7cgTYbkvus0a5YxfgRf+YsZ9mwtH3hVHfnpSb/y9ndS6Iu/UMXjd59+1vNozksuYwzezYCaN4auwIrnhzX1F9QdCRaUHXDU6Xj5fkM/Dq0F8p93xGSbXR4wAPZW0W5jJBjjZiuqGAfhOqX48HUu9ktlVJTolNe7t5NfaRKrHHT3S0X4FeY6ItxNHtgXyQETTGW8vydwNk0kniqvTDIHBCEarlojIr2fLrlxla1M7CO22vzGY05qyQNKMVlZ+3JWkGlsm1XKFNZXPOIxm6R4ygWiWjIC15o2cPUxjJ+t4dOGI+jYmlqIh21mu1jM5hX7v34BM4SB/Z7BUA3Jd6gpGTHb6tdOqFBEZgcIDlG5N8ufdqYppG9gej0b91gPptG4vs4MxApqF5yQ5kpoHNuXIbE22ItGC3OZe1joniAE+KdT4amieOmaVDFt5pzw/83AIoP8LWW6QnLOvim6k23r1yEGCG+h+KI37r1WZGUEMWh4m4+dhSE+ww55FRw4siqRNCIk2zKHo8oXWHbZtZpjuXspg32Sm7yeJwW3wf/5qt43VM7FxbvPq38jSLEnv4fStccrHi6cUafydrIRBZHQxG9HCssypw8jKzEHfbKNUQ1i15OPLk6PVHEaWoauaw6mS0ZiEjMgYKcc8FDoblRtx7v/fGWOcIP7JGcrcwOWjPCmVjeOM4bMsudm7d75+0AXZu2U/dtnq2pGeVQP9zr2s6ZH4uRG3QUVkPURKcG8Hns5yeJ94c8etRInrcZZJbz296z2KGdlZV2TClMlMKZaCyefqjdzLb+ouIHhdEg3FESTJVmeatGp2j1Yu6NxbYJFMOO3qL/1ZkK0BTF3WfDXnXlAeIUArKtJBKsBS9aKrGrEj1GXSHzbBzAB0F5St09FTcZ7byTsRREhVJGf/UHmwp+R1raGYuj7KeHxpZDK2n5KvJZUxgsdjEHtmdIwc5EwBTWoyyjzD3I4JL5zjAfsAlgNC/PIVHWIcpba765BnUa7WuHTblcprzdgDOzjSEyuzItKDXGGQck0d5mX/gMp628i77cm30UmdKUUgCGJkWjOUJziK/yAbBVQrxoidKyffTZ/M6+7hAncYtsU5+5wiakktpz7SEgFg8vmotmYzEAfdse67qfeZjdDoJn770DdHLNwf1AXm0HM8F+qrlljHnWcm3vitJPCNJxAL49RFljk8u9n8Ouse9esRWSr71XVV0cnxVGExTJtttyUYnxhC62KV6yWLTxW72yCM0Dxoc6tdSmPOFLtzttuS75igSjOU6pnCvtQGT17h5UC+TCuhdz7Dhez/fBzfPYWzuTnvtL1byxexbkHotsryoMkvqJft/XeY9n5X0oNj9HVWvLtvh9RFjuLNT8bv4Oppi60gW9G3Jup3gq8O3o8yBtlVJTolN++1yj58MLpRC91Xwjdz7W5TLKWjLO914rtpKetVKYfme27tUZG452Su7WcybxBBcx1Tu5xXqqOFbGZ1Ypo6rENRHrEUb2Mde0cuN6nzu5zU2sI9zGM2j2h/5e03w+4KI9+cXJ7mKZlLs5iC9jNZSKBQb2MNeergpNZNf9K3y7UTX9QitfW8SJ8Z1THVfALDZK7up18IvT4VQnUHJv5SFH9SGxHIanepco9ttyU+sUdh0cwPvYjlv8NXMKD4f3+/bukE6PBqIMfhI7mogCjgkP9/c306Px7k40Y0gn4Nd6rumcnHQmzWNC5Y7vOXEIHZsONtRJ0dbSu7pS/GTZB/jYho1mTQddPJJZvAfvMTppH3kt6ORqNO7ra1lnKH5CH1B+eKLb9G2toOnxw7nrH09fPFIZUjLU0UeHDOOzdd8iIvf/X5+8KdHGPP4b3mwfRu3vN0V+18sp8Sm3W5eida186jBSY5GZ6D1BspW0GUWFTFeylbZ6PECPVeKo8cLJTJiPTISvZAkROAUuMfrtVoa5RDfRd0jLQ7Rx0hqOEgP9Vp0dPu5Wh0X6Eks4BbelfubzSQ965/6gveincvHsERuwkZya2ILqdirtJvzuSa1hgusmZA9xC1a3k6UzEg9z84sxNV5TBQj6ZUWB+nxXwByLSSW6aQ+8UIoajgYDe/9XNi3wWh2T0qBbIyLaZzvbTBiJD1yHNvFGiYaum9rl3ieOq3HxzE3NC0UTR8sJwiQIUwT4tH1Cf7Oq1uQ5GRHOs5CvQbIb2DVAIU4Ofd4qL9zJ8fQ/bXK318viu/jD/YElvMW4xnCZ5LtgFZVeaXq5aLJhSFmpVl6uWzpy9Dy5h4Q1b0sHEvdTpStPinZvejvmH3NXwMw82O38tqoekZ9/R9O2vq/HWWeaFunxKbdlGxFDhl4cJVKOdAnrQsl5zIrFdl5fMo8toh17wTnneajXMe+i1p2Y+EQR7FTmHwmuY4O67RQPm8QBEOPKPN/0so/9d23xOGSixWrlyik7OSTqS18xxzBWtXDTEQourfaPvvwkCQ/PJxb1IXDp5Mb0LS1fpBSQjvs1yuqn4K/qyZiN+ge9/rTm79BuFJLOHw+2YvCPROPNpZxV3ooP07BI47gung+Gr1BrSi6S/XK9kB3dLMrsj5eBLUHqKObe0MkJ3F7EvHkQb8eUe2MEj0ZL0nIVuoEvE9odLLLd+F7cLBHiyMPsNoRRTZnDUr6YEXBILrgOnIsZR6t3vG0ZRS4a41UDaYa+DILQV+iJArQ5Wj1gjrl9oG3o/8rySmxaR8vcJUosJGgnAh82oHEvz4a6W/0+NHYAqpyvXs82NM4nVbe4ur4Wbxur+fBjOCSgsoEQUFSgROx93JwrZzP7+xWFhnQ+RR887uS9z8naK7phAJXb1Q7oDSCnMDh5VzO9G3JI/yLOZQn0tOYrE306+aBphyvjAEPGjOu9bLKEXm4Usdw6xJ/k1/bBpoazkKa+L65iR4V4y11kElGt2/DA14JAsu4f3NBd8bKx4hr+WAob7PyXk72WjNxRJ0fCX1FXOehrM0sbSIdmWlFAY2eeDnVwTILo8ej+r/wBFwvOpkZ15kZP7ZxLZSZcVHSptdvngelKbGYuNYbGUzWnzJPRrEKNhkr3ceNlUG7QlIKI9wTW9Wy05xbcX04XmBX5faBtwPHvJKcEpv28QJXKeUu9iRupck6lcusRq+UztHgig+kOyaIUX6s9dphzkNZ8ch2KvI0ilHucXChCr175PM5HZRgihrBv6qnmCVkyUD5YN08966u6ezO1vOE2Ivm6Fz8lKK5imXTcxt7CHKFgYCeW3i63sv0mk7azfncnlzFHquOrMq3b0fmcuJ2H6NjKzECNqJqcDRj7rnXd5jzqFNDuFkDQ2vjjngn7enhWE4L9WorIxnsI8wdpAfhTMOSbT64is/olqtjCD8656oOXoM0JtrYa80km2ODA4hnDtKaPYcpmhtBfSFg5cY7eM3itSGYUx3TtHyZRgxVD+zO97OhdYf6zD8Bpzr5SnoYMSloy0pa4pWjpb1yjmZuB/W8fnN/tv1xCILuDHSZx6pzNLZqNI3Tf/HvrNq/l8nv/gDr/vQoscd/y7CY3q8yveeplMStNFmVigS+CcrxAkQptw8cy9p+tLb+IqLH4eRNhg/qlYr2bs+0oCVrGG1H/z0oJ9odU61LuBpbf67Zy87Je0iog2TUUCQxlILTN01gct9YIH9S05NGSRxegcMe2U0rbxEnFkDh2sJVnFkU7BWqmxCgXBvXapfwX9Yy7kxJUjEHma2uzzBFKbpk31XsR/VGkSDnRE8aYLsnb0vWVswGqFgv8u7xoEsf3DkWrH+j0caCzAz+IPezR3bjCJv3iXp/WSzVzj/LTiYJNw0smbLAVn4Uup+hIMLfU0aMR7Lr+SrTGJt4iYTWHZnR0KEZ/FvcYeR5FhPp4tsIQJFgCSPXjWfeoemk9mVAKRDC91gE+2w7ijtqOrETSaZnD7kMbznqTUMLb9wDCRQS1DugL+WCeDhro2heDHCZx6pztLau37sd87++x69/9wAT9+ziI4locJty9jxQo1JZLu+Aq4R1KskpsWmPTy5GyxxByiomSaKmol41OgOp15Roq9rWqSxnyTXU+9jZnYCHnb2P5lR+QW3PtKCJGswSbrPGRBuTtG52OcOJZaYwUYykT5kc5gijhRvslcdDzu8eryQlG85eEjqZnQus3wLTq4Cj9aQi1GOErhCSRqOVPdbF+VSuhMPqTB8X9MPe0UpToo1XsjCdGr9O+2JL6cwO9+9l98XWMTPu9U5NUTttKXlIbeYnyZeIaVpozgZ1vZ/9wDAzwYeZxsNqAxnT4GOGZFxEm8cZGqd321z5XsVlsyWe32HFSkH6pW1MSnX4ZUaNQVtWUpv7f5dt8oEAw9uDWbipGK9nwCWEvHYCxvVkkKSmcfPhvZConvIzSkr114lej092W5XklNi0FeCY1YHOV6NXra1TwYV1MpV5up5g+TLJ3Nm2n2K1rhUW6knSQMLJghI0GMvJyqHEk0dKl6d03isU/0H+/vLTiQ1o2kbaMy2MNlb5QDXp9FAM1cewEsxcQ+8EKfL1N6Xk1yPHcP3B3SQ1LXTYLJwbpc7RXn94320wluVeJHIoaKKbTJ/Gj6xh/DjnBq5kq5xU0vE3k75OYrmCyt3LBu2VRBVzDjKuwjrdqTRmOc05xLQJvMgmAF72ccDDbfgHzebulYJLL86n4T33rOKz6EhDJ90whORbfX7Het/Ps6m5J+uY41CbdCtXq2kkRTEO9kDO/+225L5DKZ8aNYi8FkwvO1mfzbfD1v+WMt+JHo+Q7eZVxMQhzL4qgE4GORX1qtHpF/b42+zCOlnK3Nt3GVOeyfLMcyt8ko1xy+azUw4r0i0EfgmKdxe6Kb6bvWovi5jKL1nDH808h64XiCZwSA6yfUCaV5e0Mnd2t78hvLp4CDdKgZYbywfHjKP93R9i8jVu3um4xx9hTvsZeSanVI0/N0q5s4P90ZFp8b/ruf5Wp6ezmM0khc5CGvnXdDu2OMJNxt7I/OGjcY97UrThZt7AlGbRZlKuzFJR2c2Dy1+bjItpTLf62Km62Cd7yOIwXfQxVoO70iO5J2lSI3tBuYFn04ckWdXVy5rHEjw9x+Sy2YKnnlNMa3Xtbbl7EXbzGMZ/8qfs/OI1NPzrEyQ6DgLw6yx8IBcA9Z4Y3G2JEGCMGXGdMZDzf1xM4yyRp0b1c7dj5V3yx1JmtXonq63/LWW+Ez0eIW+Xe7wQ9OIdKS9NiTbGJ2q4L0iyUQL4xRuDqHsub7M8M1XDu2N9wFY+lWkhE9j8vY29MdFGKuH446mvlDlOaDcv+4rnu0hoGlDj553OKMg7rfvObSQ07aiuMUIsUDmZBOw0a4nLqbkF3sTWojfsY5WiDVeOx9bW9LusqLxkqrhSaNa7WWkf4ExG8AYH2KYE7WYOSWzQBsxDST4q9/Gj7Ej+Q5q8S1dcaZs8t1Ixd5Zi5bOKz3kR1hmbRPtBhISJn7s/VE5ShLnTJ+ou5WYhYEx/5ZUsTK8cQwTkqVHDfTTwY/qOvCPl5JTYtPvjHrdltmKMcDU6A603ELb8tJx+umOCh5By0cuFh5VC12aUFH4nbTvMzpEWvG+lQAEZPY7h2IiAtjeejSXuuYJjLnJ67TnoT4UWStNwAqAX49H4VYDp6OysTnM8X/+ovNOovqgkhXqF/fAh4wg/MjvZo7oiF3hPX1RZZjmd6A1XKyqnnL1gXvL/MUfwkiOYGfHMvZKFCwMbpKYJTtPS/G1iLd8xRzAMQQO50+juZp7jTR4SDSxkCt87vJHdqoZPxPdzaDF8XUnST4IX2jDhc/eDiIEqjiL20Nb8k7WEsRohwJj+9BlEXymUk1U9ZmQ+eH/KPB56J6ut/y1lvuMej5Bq3OPZn45BjYuTOpAmXVf+1bkanYHWOxpbyohBjqt54qf+m+R211XYX3dMtdHLHYFTb6FuqTJvufZrbBs2xv88wTrCFmMosV2v8ruPT/N/f+eDLzD2UD50Uq+pIpeyxmCsfIyE1kW7uSAErtKeuaqkrRnPPsY3v5vmvLYF3Ctf4Sf6QYxBNdDbE5l3GtUXlaRQL/hCAYJ92h/p1Hdze/xA5ALvudVd2tCjd4+Du+HuUz1+nnawrPYABWi5+gfvv2/WZmLZQ9lquP2/x5qFpYZiS8m9LOMn+kFiuU1yZlwwa4iFTLv35lJK7jE72S17ceI201N9GN3n+y5lS7zO2Tm0tu1POcQc4W/a7eYCnNQof8yD4qGtnSlgk4KPDqkhljF5KCtDTF7l2ujJ0V4pzBpSk6NGLZ0P/o6r939fme+4xyOkKvf4593//pIA6t/6yc1kxlWmaqsk1Ua5Hk007M8fuSv0uVQ7t5sLsBha9PvjIbqlsf3J0+iVO1jINL5vbiBj2Uyv7ebw4/fy3MG9nHv1B1j35KNkXvgtG2vggupelstKY6LN3+Cmx+PMHWT6m1nhAj+Qkccz44J6ZzKG9gJ31prI9MDBeCh0djt9/InNJHLpd6WCsCB/Yr9BTOdX9qvM7OtmlR9MKBkrjtAtBbWaxvvSgkeqhCY3NI1hyuGH2WF8JHbQhWKFkht2ORmoK4VTWV5JSjZMCM9KBZy9w2JaBL76O3LyyCmxaUulYaXjxFTlQLRyegIHIU4hd0xwQVMldAa6zONsa4l8gwXaWZHgKoUShUNdLC7NoRe5rdBpjMU4N30awxmeDxoSazkrK6hfuI2ZF36Pn//+Ae76wG5ebIThd1Zf//7onay2qtNTOWrR+YzUoFH2+rjwfWpCjv4yi1Q9WOk4cTe12j+xbzc1bjeyvO4IOp2cSzkzgumaKHsXHTXmwZPx9UxhjbOBVYcsPhbvKbvRHu2VwtH32anzbA4vkWkx7K4s5eh+j6XME2Hr7SjzRNs6JV4tNSFJprJowqn4r5yeh4NRjQtioPWOxpawbISZdf+paJ2BLvN42jKlzfN0YErbxZiuIOV0XFhIm6bEYs4c9gSTUo8yKfUoZ6QeJqF10aLVs5MjtMqN7KCT64ZkaUZjfSskEoK//fBeEgnB+lYVYpA62naOSy7xXbpekNyx9Nkr2WKdcva88svVq1KZ4e90kkqZNCUWc0bqYT6Repmd4hCtaiM7OMwtqbVueblnsxBjRgg3j35mXHBHTSeGpnHnCIvZCY06EX0XLYQklTIpjBAYF9M4U7OYlLsrn0gzkw1Z8WRcro2eR+ATydfp1Pew2qm8UZ2o5+RElOk9C0q5fa2UGwMyMTlowOpVrd6p0mcni61T4qR9vEQasbIvldmmEejbKgfAVaN3NLaa/+HX/u+FVR1V48koL5gO9ztrGYTBjUzjfl6ht8diPpM4N5fC1V/xaEbdDbI4yn9PbDmHs0P5Ry9oyJJMB+Y8S4j165KVA+NKlj5sp1e3YspNXzc374TlIFSxf9iSgh9ZQ32Yz7dDCiPpowg0CoOwCr9fSlriWuRddENiOdKoQ5fdtNUkeLNhBGfsOERLX6boZPy5mjRVwD2XlOAd/p3DBvZK4VSRyGehykj6d+Ttk1Ni05aGTua0GuJ7Km961eh5Opt/eCNW08iSelpWIqu4M6tGrypbQkAO2rFQJt763yTb3UC0U82dNCup83ivwVQafdrNNeyquGGXc48HCUMSZjF/8RjnSm5PvoCheXfKbt83o3F/ILp8UQEq29G20yPw8OpWql4yrrP5bnfeTfjs/SQ37/VteW5gTQ1jIePCd8cncMy9DTWoUy1QS7VlRt1Fezj4D83dznk3X8ylzaPYuXU/3/v5SuY++2bopWFVj8nMCs6aU+05OdFlRj0Lp1L9T5Yy34kejxJLoXfqkKkctaLvjdAzROhE7bkgCnNBC8UZPxp9256KZUbpbb57EQ3/+jiJ3EZbydbmuxdhNY1E12I4MuK+t6D+p1q05U1M5A/sY7fswkZyY3IGWMUoVkEpR9NYqkzvxNuYw3z2qCGDOnNyKWl/HXHKjqp/FOOUpxc8YRcCxRTWC0BkHc741M9DzEdaytXLB0iNzkG35gE8ykWPV6p/lLwqDKap4gUiCBW6YEjl4EkPNKU/fVZKGpIrWDJ0BOfd/G7GNrvkI2ObR8FNc+ha28EdffmXhkqBnUHK0Epn6JPpORmoMqM8GVF6hc/CyVL/U6nMd6LHI6TdvJKYdQgzXQUimuYU6WX/r5sO5smJSPlSRowtdy8CXNCI1P6+sraUcUoMxVHLn9jORnr4DLO4hxfQrI28mzOP2a5Cx1Ipn/Kuw2xhtPE8y2PjaT9jNzXaATThgJ5GOZIpW+ACU2POChm6y/bk5bjFn5tlaKFPO8M4d2uai6xif+yOzOXUx190y86d/qNkR+byUNpdhzk/j1EeeDNZaBzhnnSXm98tjh7Ao1R08JQtMLUP7ulL8ZNkX8j9XggVaklZdoHw8pz/Q5pFNfQ8D1H36YXibfAd5nzWnXmIa3IbtidjJ4zi6bHDaXmz8gu0Jzsyl6NpNYySyyuS9PylSfDFK4r9LCjNlH4WBkqCNKzvyLHLKbFTjE8uhiN7IVX+ZAaA0lCFep8vUEnEEZnK0cuFesrQI1mdMk11JNrDpOveyRmg+UsPAqJIJ0rfkSXo6U7x6PG/1idyTS7P+ktc6uafy/Kek2qixzsyLZAttlVr1jDjhn1cOkfgbnqKp57NR4rPsKMXqWHd2Yio2kO57xVjTPuEIbnAuOBpO9gXUXe83gYuzbzd1Y5gH0e4WtvHMpX074772/9R0cErnpX8z+1DaZODubhmBN85qweJZKywSGmCHY5izlbAcqFCf9XVx02J4n4qzHP+3uGNmGRCKWCFtJyF/RGU4NXC6dsS7Ny63z9pA+zcsp8zdh6uypYnTYk2UBpoldeMUrYKN5vj9ZyUyzXvb5lRL15esF8pW8FnYaDXFquvjx9Zw8rGZ5yK69nbaeuU2LQlGjIxCs0sven5uok6tn33GjJNI0rqxPZr2KMqP8xV6Rkaeiwe7dLOyZa7F6Fr5XU8KeUeV+i+S1Um6qrui6PVq9a1WUhwX7rM8MuOnoyXpOYM6ijHa3c5CsywLYVOgxzK0iVDmDu7y8ch9+/tPFd1ro1BV+7E5CDua+1h7mwVIj25kTjBmVDYTkWMhsQK9lozfT5qOzEK3dyb28iL+8Mb06CtGXGHkdkz+bbs5sepg8RzC13QPV5Y/6B44+TdVwbb8edWxTQgTjMjMjVccMM6Lpsj8NBNVjwrGXGna6dW00iW2PCiscrDec4NyRU+osMNAAAgAElEQVT+tUFh3YL1915yvP9nZcfzyM9fgJtnMbZ5FDu37GftL1byxb5MpK1ycixuy0LO7+NVZqVTcX/LLMRoD7KfnUhXr/9ipyVZyFllc/vfcY+HdSrJKbFpb5fXIEfbOFsqd4w+ugb5mT7ilNbVJ9QgqrFVhZ790zEw4fj7fnZYlyNyqF9RVwBRcix65cgyQvXKuX0rlRm9cVWWci7nSt+ZsOwinlnQyqVzCEWKF7qqPRe3194Jy2fxzJUv+qQnTcuuZLMcETo9FrZToYfquiNzOcmYzSi5nL3Zi3K46eEt1tPXhYFMp9kju1kmXkNXW3MgJm+QwUXqag58r7D+pSQqOnicF4XtdJNdbHDpbMvf1FcuVtwiBWguZKgpSrtMw9HcssiNX9inQQnWf2/2IrKqhh2ZeS7POvD+Fefw0stvsKl5GZN37ivasI+nlGQ8KxiDgZByp+KjlUKM9ij2sxMh+Re7yWUJVt6R/ssJ3bRt2+af/umf6OjowLZtbrvtNqZPn17xe9n7xpC10q6rtEJUSbZE9HV/df7/9s49Torq2ve/qn73DG8NCjLjEB4C6hEUFAgTlbkqUV6SAYKC+ElC4keNr3sRPV71aMxRbg4BAkqM5B4TQPwMGM1J4hWGh0GeQTASThIeEWYGeTgwM8wwU93TXfv+0V3d1d1VXdU9XV1V3ev7+fBhpnv1+q3a1VO79qq9187ETjWlnaFNOrsB7lp4fJFJbRzjAZ+oGFlC8yg9KlCCOQFf+vjU0jbytC+T+Up+lppchlRvcRUOoagP9dG2mq/+Yi/s2cRFZ8cCc6OdSnKqWv67KAgY59qHNbJNTyqZA50IY4Bnc7wTSjoHycc3wFMLD2sDeBFlnlrUCRMRYL1SjoFDGI7AefT37MQgvjWy0UhYGsGWxy508vZXqtcuxSEKQuxnpdnB+8IsMgvb14z/vdWPP90ZwDejnfpdu6F7E46EJWCB1CVg8jaV0szJjxSSf64TJoIFXJG2CLUCR9X1jUpbqu14lnwOcqGZblSciT+5TUqN9gwfq+Uy1Ru/sVOuv2+Epl47O/vKa6f9wQcfwOfzYd26dTh69CieeeYZbNiwQfNzrnmn4fCHEFjUA+zq9KNa1dnXGdrk2q6rvuqDVeA6gglbhtYn7ZClVC/8RPO4lF20klHaqlRP7fHkZ371gSrwPj8uDydO/qkPVCHIuicep0Z6nEMYXl8Il4civupiu3xxsfdjo15fOLY1p5zV4lbM3+nFK4svYfoBHwDtJYPScQ7cdgte8e7GmO1j0clKkHzzJq/X3te1FxwngkvY6IIlthnHgWPKU+EdXjei9ybRiWgtOC22IsyFYhc6KW22n7XhYHlviJwTPu48eC4MBqDP36/HbaGT8JWEcaJ5XGyUKx2HNDtYWrrF+/x4jWvHms3ABFmnXseJscInXr/6DO2Edc69g6rrnOVpZneJxmx0jktoi3QYmbZUq5aWa01voE1zVJypplSjXenGK9+p3v1hLm39fSM09dpZ2ZcWee20p0yZgnvuuQcA0Lt3bzQ3N+v6XOdv+kHkOsF0dHqFygBPLbzRkbZUtENP/Wo9NpluRwkoP/NT29qyzFMb24Iz3YhZTmTLzVDMV5mnNlK/PDqajdxUXFSN/3UhjEo3hy0Cw/jtHCpcTog6Om2Jca59cG3nMNq1L+1kOKWtOeP4U+yUSC5icg4teNpzAT92hrHqGoaevAg4IgWhL1xiGDfnAr4Zm2DG4+NPRPR68fPYFqRyLek41GYHJy/5USt8kilKaeZAMIS5TvXSo9lsjWoEmRaSyZZ0o+JscfM8LpPdeHU13d4Vxrg43OJTr79PZEdeO22XK371e/vtt2MduB66ml42ylfeNC0yezzdM790KcSr3FsBcKgLVKGTddeZHo9vzSkyHizpj16eEpa/1hYKoxvPYZKLx2+YiGECgxjOvC1Gh3jF5SoHOoFRnDFpM/nmHz/ERTR/h4vOAI8c7cefcPhsM0OlbIKZvEiMkqbSTHnJTmnJj9Rhd+X7o5hm5rSfaVohbalWSCbXmulGxZn4S7ZRu/EqhkInZmgWTO3xmpoazJw5M+Hfjh07AABr167F4cOH8fDDD+v25+D13V/osculr7xpKhSH0cKImrhKdaCH8sHYxVjNV32gCseFe9EZTZNr1R6vC9yJLzALQbF77POdshQ7gwN1wp0IiL0Q8lwemYUdfW15J4dp0Wac5gSWdWbXFpFsQk+ERDHlNdHtzshXNnZq9aFv+4TDjl2R15JLsWajKe/UD3hFrB0uYt1wEeuuZVg3PPL7Aa/63Ag1zWp3CxrQHK1XHqn/nklcXbWzek3ryKg4/Z7g2WoqZUoKoc2sqFkwtcerq6tRXV2d8npNTQ22bt2K119/PWHknQ4uEILn3Hnt4iQ8pV/yQTY7JOUaaea1NEtb6HDEUu8VjsQZtFc7UjucdClgpWxCKwIIsDD6cP7I2uSW1D2YUwqaJBV0yQalGeBapVi7QuL6boZ4+j1zXylp5mj9dyJOrh5HEMVDXtPj9fX1WL9+PdasWQOPx6P9gSiDF/xf4OxZgKnf7R9ffj8CZX3gbjiHjoGXKRZBkWCCAOj4I+myHccB0ihJy1c0Xqunx4H0z/z0auqdPc5cDojMkZIalxrkKs828KIIp7ctmnrvhi/Cic8KT4RZQlzp1saKgqA6g5gBcIkVqnswpxY0SSzooka6NkvsoOMz4NVKsWq1f+zGgmuLfaekSmmjBF5xfbfWjYGaZmqaWftvyappSyM103XYVkzPkqa5vvLaadfU1KC5uRkLFiyIvbZ69Wq4daYa0828k+qIa9Uk1mvTVTvpJuLrj66Bp/58Wl+SLXM71Qu1WKj2eLrNI9R8yWdXMzjg8LpTZo8nV9Hy+sLgQqHY1qqRG6CIVpknvuVkpG3Dkcp5AB4LMfw2xGGuG3g/BDzmiseltTZWslPKJggiwzM9m9FwtgXgkJJhyKbD09P+UgctnwGvVn5Sy5faPsryG4tMd0LL50xcaVRaDDWtzdC0e/xmaBb07PEnn3wSTz75ZMafE8EjxLrF6kunQ49dLn0l2Mk2JpFvqylyjkRfSRuYSLbHfz4X4auvUNRgzJGwOQWQWo1MQqo01ZXjVKq2pYUY1XSgPfZZKcarPNtidvVCFcJIzbRI223G/EW3aZTeqxfiS8ekamJK7eF3OnBRCOLDThGtIkOpO95OetbGAqnZhKVXVEC459u4/VvT8Lc/vIdT//VH7D+dOqt4bPR5s1KHJ6+8Jj93UvvL25zBARb9/2oAE3Y6UYHIDHjpszfpm9uYgJ4bC6X0e7pa5vlKeSdsZkJbSBJFii0qotUJ/wP8xVMIhks1bd3BNk07PTbZ2CVvTCLhWnAanoammC81OwDwHjkF4apeAKK1zqOjwIbAbbF12h7hUuQ14daU9c9AZDnU2eAYIMhnfZzJ67T1pG2krRX7OvfEPqsUI4MDnCCAQ+LjDqkqltQRu4NtMV8RP90gjcQbArfG45S1R4D1Aocw7gSwOrgVL/pEAK2x+LUqRkl28mzC//RewKr5i3DLpKmR9+YtwGeX98W1ry5E8jIWl1CFv27akpLOluLr694nO57useMMhksT2rwhcGvsPPZ178ONwiWIEBLaVKkimp7zpGcknZx+TzdCF3XMpNcbm5pNcoZEaG+HW+Ou0u5pVzM07R6/GZoFnR7Plqu9H4Hv0ZKzlLZh6fHH0tj0kPlKZyfTPPaL+QiUXwYAGOCNr9PmvRGbsuj2k0qUebfk9Dj1pHaUNNVizPQ8yf2cFO5I2EFKrT3+XSF+rbWxasfp9vnT/i5R5t2Cb+wU8cpillLQRR6f/OfktuA4MVIBj2+N2fHeiJ7Y0Y5GxxbVXZP0nKcK8FhTy9JOZKsAHx3dh+KfURmh5yPVmJIhCQMPaFy97J52NUPT7vGboZnv9DhNWSSKitmuyJpYQLtUp5xg0h9b8u9yIs+bOVRkeU9clqamuNIytGyYsN+DVxaLaZ9Xj0biXBNphA6kLjUzmpQMSZpJqQRRyNim07ZqOsNQTfko0EKzx+2oKdlorY1V8uXnefR7+w3s+/WbuNj4Ffb9+k1c+es34E9TbWp0SF+tar3xf9HWjn+74MMSoT+qMQo/E/rj5fY+OBlSTu9rUS6EojcW+mcuS+vGRTEyypY+m4vjPNCZ3kbKkADRzUx09Nl2/86aoWn3+M3QLJjiKrnGqovhDdWUD2QsUlzFrppymyoXj1YAEzMoQDH77Ek8vvo/0LygGo+v/g/c1/JVTuLSa1dR6sdQt6ha1CYbTbU9xdP5kp51d7Wgixx59kC1/ZMyJPd1V348oUfPSDu7a9o9fjM0C6a4CkFYGa1iFjXdwjhxTeJyf8aAq//+Jbx87oqZZMJMfweWB8wtaqO21CwbMqlPrlRTmxLkRDFii05bdDsQ+JofrjPaGz7osculLyM1WUKPEf/RjukkkfHgIILjIj8HO1xwssTOj0M49j7AIdjhgosh4TUAYOAiS6Kk9xQ0JfuYzwzjv7IVGDSZQ+W4eOexfaeIjj+ztMep5CtTO3lbyW32BcXYMrR/F3rjzyEON7vT++pKbGo2ySN0tfZXOgYgvjtcpvXJ5dXDiiHtaoam3eM3Q5PS4wrwwTB859rBB0Ka//TY5dKXkZock/XUNk+P81z8As5zIry+zljRFOmf/H2eC8Pr60x5jefC4MBinbGapmSfziZd/N+AAzt3soS637s+YfhGdKa1kW0mbyu5zS3d/Xje3ww3z+MFfzNuVljzZJVznnwMB6L135Mn0mVan1zKkBRD2tUMTbvHb4Zmvn3ZotMmCDO44SNgx+7o7lq7GEZujlTkIjIjJIpYEizFvzX5UybSfRhgaMaX+J73L2jGl9gfpPYliHTYptO2ajrDUE2aPW6qL2m0LYqRUbbQycUqcpkRvx3aTM7JkIiX2/tgidAf92MM2sJOVLCBCRPp7vY5YtmD5/3NuAn6yrwVw3fWDE27x2+GJqXHVbBqOsNQTZo9brqvGz4CXvqpiGs3A40MuMvFo5EBoTxszZlsY5c2k0jexnU0KnBKlgr/trsl55pG+SoWTbvHb4YmpccJwkJ8Aw5c8TFQH+Bwd3Ta5t1OYO0lfXfXxY78mfVBnEILdyqeCg/TVroEkSm26bStms4wVJPS45bwdV/AkVqRS0dFsmJPjwPxjVe+5/0LPPxFTHI1xVLhY1ypnbbV4i82TbvHb4YmpcdVsGo6w1BNSo9bxldKRa401dBypZlsY4c2E0QR/9mrL4RoW41xcbFn1i/2Dip21F3VzIevYtG0e/xmaFJ6XAWr3hkZqkkjbcv4SqlZrqO+ipamVunOZF9Wb7N3rizHsu//L/T65QYs/e5TeKdvueGa+fJVLJp2j98MTRppq2DVOyNDNWmkbRlfyTXLvf6uldHUU7oz2Vcuj/Mgl9uJdO2iiNNzH8Lo+7+Hbn0uw5h5C3D6gYfQLnuMYLdzXoyado/fDE0aaROERUlXs1wv8mVQ0nrll5pKUjb+MJKQKGJFm69LO4UprVdP3q7U5fMjJLIUO4IgsscWZUwB66YzDNWk9LjlfEkVubLVzLR0p9649NjJa32P9ffGq0PbIEJEfy4IH8+BARj2T2CUkP4YazvF2Hr1KllJ0eTtSjs72rEy0A+hYAjz3F+h3KbnvJg07R6/GZqUHlfBqukMQzUpPW5JX13VzLR0p+Srq8cpXzc9KHAlvjUniP+zMozHVzjwg+U8hs0EegnpfQVFMWG9ejCa3pe2L90V3b5019ur4V29FUO5r8d2Iyvmc24XTbvHb4YmpccJosCRL4PKd+lO6Ybhv8NnseMjd0Jtdfke2Wqs70TCevV3ZPcblzV8gX9Z9VN89t1vw7/y90D96ZQiKge0708IgkiDbTptq6YzDNWk9LglfXVVU74MSm/pzlzNHo/dMPgO4cutLvxpV+SL9aedLGGPbDVfKevVozuoSSPwyR4H/GfOoJU7k1JEJdjenrBZSDbxZ2JnpXNuF027x2+GJqXHVbBqOsNQTUqPW9KXGZq5mj0u3TB4/X685m3Hf28GRFF5lK3kK2W9OovYyUfgD3mBqx0XYjclfTkWmXwXSNwsRGnynVXbv1g07R6/GZqUHicIG2O39O/4T4BXFospo2w1UtaruyI/K43AJWLP0sWrEzYL0Zp8RxBEKrb5q7FqOsNQTUqPW9KXml3yXtHZ+hIZD8ZSbYw4zgrwGL+dU3yWreQreb26m4/MHlcagctJnnyntFlINvF31YY0jfNVLJqUHlfBqukMQzUpPW5JX8l2SmuvX27vg3peu4CJkmZ9oApB1j3FxqjjHB1SvgxINge8ItYOF7Eu+u/cvwCHr2Po1S1upzYCl9gf5tDsOK25WYhdznmhato9fjM08+3LNuu0CUKEVDvUWrtDKa695g+h3M1D7DA7uswQRBHr+1yJ2edPQyqV0ksAhs8CKsfHO/ePPxHR68X45yIjcDFhBC5njIvDLT4BYkfkObfVziFB2AVbdNoi4xHscMHJtAs+BztccLqcaXMIga/54ToT1PSVS7tsfDFOdmHrQnpcZDwADhzC4BSulcl2AFJsu5oCEhkPDiIYIhqBDi9cLJBgI2lKcQQ7XHAxxF6rC1ShMzoCZXCASe8paCYfs9EpuGp3C1YIzTjDWhFCGI+4WyAK+v68JF8HOoFRrvQ2sd+j7Zl8PrtynO9cWY66b30b10yahqV/eA/lH76H73ScRAV4rNksYsI4Bo7jYsvDRng57BooguPaYt/P0yLDbSc4QCEMK6YaSdM4X8WimW9ftui0eU6E19cJsSOsaRuxS7+ExneuHWJAe5lNLu2y8cXJH2wmpcfFpOpTSkh2PJd+HbAeu0w1U16P+uYg4qRwB8K+yxHuiN/ElHk+goe/mGAbOZeR9+tlHXbET/ymQkkz+Vi6Gr+WnbSUapH3Al4VemN/mMMtJfp9BS+1YWWwN37huKBqAyDmT+1cacUv3Rgk28Vqh0+aCgAYM28BPru8L9pfXQg/z2P8J8COXQyV47mE5WFaI/BMYtNrY4avYtG0e/xmaObal6YfTQuCMBgOYSTOurMfyWuvtbaglDgZEvFSU0nC8/DV4SOoD2nf4GVK8kQ5AKj1e/DGkCux1e9JqR0u/70CPA4nLQ+TXsu0QAtBENlji5E2YN10hqGaBTx7PCxIa6MYBnhq4eFb0/qKd+ypnaGdz3m5k8fgsAAXros9D29CCwY4E/80uxKXvOZ4NYbhZ8LfIASCCN90GcY9chsqKy7H8b+dQuB04igguZa4tDxsqmx5mNoIXOKAV8TfBkbPWjSNnlzjPFfHaaSvYtG0e/xmaNLscRWsOtvPUM0CnT3OcSJ8PgEcQglp8XS+yrxb4Oa07boSV67t9Pqa1b0zYTnU7dyVir6y1ZTXHJfWSfu9DOMeuQ39Ky4HAHx9WH8Ez+zA7mjt8H2/fhP91/0SftmEMqXlYRXgcbiWUy3QIk1i+8FyHj9YxivWONeKP1s7K59zq2raPX4zNGn2OFHwlHlqwXv8EEXt50DFwP6gmPA8/K/Mi3451kieKOe9xhvrsCWmzBmGrQ//Gs3vr8Pj50/DX1KK5CfnSsvDJuz34JXFHQkjcAm1SWxzoT2plCCIVGwz0rZqOsNQzQJOj1sx7WSW5k0IJTwPv95xmaKvrmgmb1LiPPYVTn3xVYLNqX9+hWGnWzC/6Sy8PK87/nIhpFqgBYin0AHlGud64s/Gzsrn3Kqado/fDE1Kj6tg1XSGoZoFmh43Q7MQ4u+KZvJEuYfcLhz6z52xjvvUP7/Cobd3oqo9oOlLSVOtQAugPIkt0/izsbP7OTdD0+7xm6FJ6XGCIPLCU3uPofZQPf7UvxcGn2rCU+0B7Q9lidIkNoIgMsc2nbZV0xmGalJ6XOHV1KVh9orffF9yu6r2AKqOnjFcswI8xn/MUKFRCc0ObVbImnaP3wxNSo+rYNV0hqGalB5Pea3MsyllFrmd4u+KLyum6jKxu9lZkldNu59zMzTtHr8Zmvn2ZZtOmyCsjt225SQIwn5QetzKmpQet6QvJbtItbFIGVJndG2zleJPKHICAFwbGFMvcpILzWzsrOqrWDTtHr8ZmpQeV8Gq6QxDNSk9nvJaXeBOBFmPnPjKhZ3atpwnQ6Kl0uMJRU6ihU7SFTnRG1eu7azqq1g07R6/GZqUHicIG6FUbWwoH0S501p/WlQnnCAKA9v8xVo1nWGoJqXHLekr2a7a3ZJQhvTb7paca4pC14qrAJkVOdEbV67trOqrWDTtHr8ZmpQeV8Gq6QxDNSk9bklfyXbJ1cb2h7mca+Zi9nhCkZNa9QpmmcSVjd0Br4i1w0Wsk/1bO1zEAW/uHynkylexaNo9fjM08+3LNhPRCMKqjHFxGONqBhCpNqa0E5lVkIqcTP/UByCoaW8E0vN1xX24faaERBC2wTYjbaumMwzVpPS4JX2ZoZmL9DgQ36mrXNDer9uoNkv3fN2q7V8smnaP3wxNSo+rYNV0hqGalB63pC8zNHORHpcYHeJNbzO15+tWbf9i0bR7/GZo0uxxgiAKnkw2ESEIIo5tnmlbNZ1hqGaBpsdFxiPQ4YWLJW5QwSEMTpZRkPsSGQ+m8qy4oM55HnxZRVNpExE7xV+ImnaP3wxNSo+rYNV0hqGaBZoerw9U4RQ3CceFexP+BVl3AIAIB1iSr/pAFTqj75sdvxmauUyPZ6KZK19KdtLzdfko207xF6Km3eM3Q5PS40TRUy9MRFBU7qCJwiLdPtwEQaRC6XEra5qQHpdkkhPRudYMC9q7a5je/hbStHv8ZmjaPX4zNO0evxmalB5XwarpDEM185weF+FAnXAnAmKvlF2rc63p8LrSvs/gQMhzucLu2fo0pRR7pnGZfs7T2FgxVWdlTbvHb4am3eM3QzPfvmwx0hbBI8S6gUdA01aPXS59GasZ76kZc0CEAxzCsddEOHKqWS9UIci6oz5QhTLPR/DwFzU/mxxHiHWDA+2pI/WEWJUnlDE4onYc6gNVcDjc6C/+l2IcLNopK+lwCKNemIi+7j/DwzfltdSJpK+kKX9Pq83Ujk/yAyBBR/7dUNLhEAZLik3Jj1xD7VwSRCbk+jqVCzsr+9LCFp32SeEuhFpaAVHHuCvAadvpscm1XRa+gmL8BNYFqsB3dGKApxZ8hwgXA+oDE1N2vDIq/nRpm3pBFkeAQ5nr/6V0tAk2AKCQHq8PVCX8HhY6AZUBufzGQh5bvTARV7j3pLVJR7Z2IuPBQUR9IHqzwDWl2EixefiLmm0mxe7i2sBBTDnGIOuRcGMV8y20oV6oStEp83yEM8FbYq+LghC9SeuheINWL0xEMNBT8VzqaTOpPdRWA2TiK1s7u6ddzdA0In7pe6aKTa7H+fI1SsONbdLjWunUTOxy6ctITaURDocwvL7OhIthLjXVyFXaOxM7vb6skILjuXgHJS1dy0WbNQRvR5B1MyU93pX2rw9UxVYDZKJp1bRlsWiaEb9drsdW8WWLkTZBFCtlntroT/6c+TzgFfG3r7dDCO2HCCd8XCs4TsSwfwKjBNvcxxNEUWJKp93Y2IhJkyZhxYoVuPnmm3V9Rs9sY712ufRlpKZaIiWXM7T12mlrsrgvHTeeuttMhy+7py31HGcu4+olAMOrRVSOvxB77eNPENmwI8O4ch2bVX0Vi2YufTEAYUF7Uxq7XI+t4suU2+rFixdjwIABGX3GqukMIzXVMuBmpKrTaZZ7N2GwbwPc3MWiTY93xZfe9s+VZgV4HK7lFDfsyDQuvZoHvCLWjlDejjNTX3rtrHzO1ewOdOqzy6WmUb7qhDvwBWalf54N+1yPreIr7yPt3bt3o6SkBEOGDMm3NEEQUSbs92DHrgAqxwN/2onYhh0SHCfCzbVCPd+TGfHRvcJ2nAQAICSKWBnsjV84LsDJ02MKQpm8fjOCwSBWrlyJJ554IuPPWjWdYaSmndLjUm1wM9rM7mlLve2fS81yIYSDm/wQRYa/buJSRtllnlpcxX4PD9+aE81023FmE7/dz7nc7mRIxMvtfbBE6I9qjMLPhP54ub0PToZEW6fHgcK6HlvFl2Ej7ZqaGtTU1CS8VllZierqanTvnnmJSofXhXC79vMRPXa59GWkJgfljpv3eiF2tOckLr12Wpo8J4IDi6R3wqpmGWnq9aWnPfS2WS7ttGwia6d5OLxuzeOMFcDRoYmO9L4ku6HbrsePfbvxrV1eAKkX4a60BceJSP72TvjUix27BFSO5xK248yVppG+jNQsd/IYEgrCJQ7F1/k+aGcVCPGHUO7k836cuW6zQroe58uXFoZ12tXV1aiurk54bfbs2RBFEWvXrkVdXR0+//xzLFu2DIMHDzYqDILIK0qdldp7Azy1ONs5BjznV/2MkVwldkfjpnG4GocBXt/ISS/xWe9xKuDEjs3AN8ZGRtlzNYpuFBPV7hasEJpxhrUihDAecbfARityE4gXU6GyPEaQ12fa69evj/28aNEiTJ8+XXeHbdV0hpGadkqPJ/gq4tnj8s4q2UapIyvz1AKMB3gx5b1cxqVmNzxwFeA5nDdNpe04jdK0S3ocAPaHOTTjSyzyXsCrQm/sD3MYw9tz9nhC4aACuh5bxZdtbuWsOtvPSE27zB43SrNYZo9bvfb4AW9kprfazO9MNJW24zQ6/nzaZetrjIvD8/5muHkez/ubMcbFGa5ptC+gsK7HVvFlWnGVV1991Sxpgiho0qXos6GXAAyfhZzN/KbtOAsXae8Awjhs89dj1XSGkZq2TY/nyK5YZo/r9aXXrsxTqznrO5P4tWZ+W7nNcuWrWDSVauqLzAHG0ttJRFLj8YnGhXQ9toov23TaVk1nGKlJ6XFKj8ttzEyVjv8E2LErcuVOnvlt5TbLla9i0Uy2qQ9U4SF3JSUAAArBSURBVLhwr2YdeVG2Q5+cQroeW8WXbTptgigmItts5n9GuRrSaFsUlddXE8VNQ+A2/LNjCoI6tpYkuoZtNgyxajrDSE3bpseLePZ4rnwNSEpxa/mSOnllu9RvUtxO/cYg2ZfazG+rtJmRvopFM9kmstd6CMnfE7WVEXXCRARYL0gj7kK6HlvFl21ul62azjBSM9v0uHQBp/R4Zja5ttOq1y7tU52L9HiZdws8fKuiTZknoiWfoMZ7vSj3boKbU98rO9mX2sxvu6R6STNzX2XeLRjk+23KHAk1X5HvYXOsoy+k67FVfNmm0yb0ExmlNee0djRhf5QmqGU605xmfhNalHlqMcj327Q3hET2WDo9Ls1WdX6tFMznAtehnTpw6LDTY5Nru6x8uUrAuMidl/PybuACIXDuvoA/BAgCnIGeEGXPkLhoLUzO/TWU8YfBe72obxmt+ZxJKTbO3RfgS+IveL2ARnrNGegJztsNXDjps9H35LHqbrOor+TPJ8Qoi80Z6AnO1RfOzoi9kk1acmmXpS/pGFLaH0g47ynnSK8vmV0ZDgMojf7LTfxdsrOqr2LRzKGv2PWgUK7HefIVDEZKnbLkKftROKb2jgVobW3FkSNHzA6DIAiCIPLKkCFD0K1b6oDL0p22KIq4dOkSXC4XOI4W7BMEQRCFDWMMnZ2dKCkpAa+wRaulO22CIAiCIOLQrBKCIAiCsAnUaRMEQRCETaBOmyAIgiBsAnXaBEEQBGETqNNWYfXq1Zg6dSpmzJiBQ4cOmR1O3mlsbMTo0aOxd+9es0PJG6FQCE8//TTmzJmDmTNnYv/+/WaHlDd+8pOfYNasWZg9ezY+//xzs8PJK4sXL8asWbMwY8YMbNq0yexw8o4gCJg4cSLee+89s0PJO7/73e8wZcoU3Hvvvfj444/NDkcXli6uYhZHjx7FH/7wB2zcuBH/+Mc/sGXLFlx33XVmh5VXFi9ejAEDBpgdRl754IMP4PP5sG7dOhw9ehTPPPMMNmzYYHZYhrNv3z6cPHkS7777Lo4dO4ZnnnkGNTU1ZoeVF/bs2YOjR4/i3XffRVNTE6ZPn4477rjD7LDyyhtvvIGePXuaHUbeaWpqwsqVK7Fx40a0t7fj5z//Ob75zW+aHZYm1GkrsG3bNkyaNAlOpxMjRozAiBEjzA4pr+zevRslJSUYMmSI2aHklSlTpuCee+4BAPTu3RvNzc0mR5Qfdu/ejaqqKgDAoEGDcPHiRbS1taG0VKFKWoExevRoXH/99QCAHj16oKOjA+FwGA6Hw+TI8sPx48dx7Ngx3HrrrWaHknd2796NsWPHorS0FKWlpXj55ZfNDkkXlB5X4NSpU7hw4QIefvhhPPDAA/j73/9udkh5IxgMYuXKlXjiiSfMDiXvuFwueDweAMDbb78d68ALncbGRvTq1Sv2e58+ffDVV1+ZGFH+cDgc8Pv9AICamhpUVlYWTYcNAK+99hoWLVpkdhim0NDQAMYYHn/8ccyZMwe7d+82OyRdFP1Iu6amJiUV2NjYiMrKSqxYsQKffvop/vVf/xUbN240KULjUDr2yspKVFdXo3v37iqfKgyUjv3RRx/FhAkTsHbtWhw+fBirVq0yKbr8klxfiTFWdBUIa2trsWHDBvzqV78yO5S88f777+OGG24ousdgcs6ePYsVK1bgyy+/xLx587Bt2zbLf/eLvtOurq5GdXV1wmvLly/HwIEDwXEcbrrpJpw6dcqk6IxF6dhnz54NURSxdu1a1NXV4fPPP8eyZcswePBgk6I0BqVjByKd+datW/H666/D5dK35Z7d6du3LxobG2O/nzt3DpdddpmJEeWXHTt2YNWqVXjrrbcUaz0XKtu3b0d9fT22b9+OM2fOwO1244orrsC4cePMDi0v9OnTByNHjoTT6URZWRlKSkpw4cIF9OnTx+zQ0sOIFA4ePMgWLlzIGGPs2LFjbNq0aSZHZA5PP/0027Nnj9lh5I26ujp27733svb2drNDySuffvopmz9/PmOMscOHD7PZs2ebHFH+uHjxIrvnnntYY2Oj2aGYyvLly9nGjRvNDiOvnDlzhs2fP5+Fw2F2/vx5duutt7JwOGx2WJoU/UhbiRtuuAE7duzA3LlzEQwG8fzzz5sdEpEHampq0NzcjAULFsReW716Ndxut4lRGc+oUaMwYsQIzJ49GxzH4YUXXjA7pLzxxz/+EU1NTXj88cdjr7322mvo16+fiVER+aBv376488478cADD6CjowPPPfec4gYdVoM2DCEIgiAIm2D92wqCIAiCIABQp00QBEEQtoE6bYIgCIKwCdRpEwRBEIRNoE6bIAiCIGwCLfkiCAvT0NCAu+66CyNHjkx4/dlnn8WwYcMy9vfBBx9g6tSpuQovxsWLF/HTn/4UBw8ehNfrBWMMDz74IO6+++6caxFEMUOdNkFYnN69e+M3v/lNl/2cPXsW69evN6TTXrBgAe666y689NJLAIAvv/wS3//+99GzZ0+MHz8+53oEUaxQp00QNqWlpQUvvPACmpqaEAwGMWfOHEyePBmNjY1YuHAhQqEQ2traMG/ePEybNg1PPfUUjhw5goULF2LGjBlYunQp3nnnHQDAokWLcOONN2Ls2LF46KGHMGTIEAwePBg//OEPsWTJEhw4cAAcx+Haa6/FwoULE+oz79mzB+FwGPPnz4+91q9fPzz55JNYtWoVxo8fj7lz52Ls2LE4ePAgTpw4gUcffRRTpkxRPQaCIJShTpsgbMrSpUsxYcIEzJgxA+3t7Zg6dSrGjx+Pc+fO4b777sPEiRNx7tw5TJ48GdOmTcOjjz6KpUuXYvHixdi7d6+q3+PHj2PZsmUYOHAgPvzwQ5w9exZr1qwBADz88MPYtm0bbr/99pj9kSNHYttbyhk5ciSeffbZ2O/t7e345S9/iX379uHHP/4xpkyZonoMvXv3zmFLEUThQJ02QVicCxcuYO7cuQmvLVu2DHv37sWhQ4fw/vvvAwCcTicaGhrQr18/vPXWW3jrrbfgcDgy3he8R48eGDhwIABg7969+Oyzz2L6ra2taGhoSLDneR6iKCr6CgaDsZ/HjBkDIDIKb2lpiflXOgbqtAlCGeq0CcLiqD3TdrvdeOGFF3DdddclvP7cc8+hvLwcS5YswaVLlzBq1KiUzyZvP9jZ2Rn7Wb67mdvtxsyZM/Hd735XNb6hQ4cqbl176NChhNiczvjlRqqerHYMBEEoQ0u+CMKm3Hjjjfjwww8BAIIg4MUXX0QoFEJjYyPKysoAAL///e/B8zyCwSB4nkcgEAAAlJaW4uzZs2CMoaOjA3/5y19UNTZv3oxQKAQAWLFiBU6cOJFgM3r0aJSWluLNN9+MvXbu3DksWbIEjz32WFbHQBCEMtRpE4RNeeSRR3Dy5El85zvfwX333Yfhw4fD6XTi/vvvx6pVq/Dggw+ipKQEY8eOxVNPPYVBgwahubkZDz74IK655hoMHToU06dPx9NPP52ypEzijjvuwMiRIzF79mzMnDkT58+fx4ABA1LsVq1ahVOnTmHy5MmYOXMmfvSjH8Umt2VzDARBKEO7fBEEQRCETaCRNkEQBEHYBOq0CYIgCMImUKdNEARBEDaBOm2CIAiCsAnUaRMEQRCETaBOmyAIgiBsAnXaBEEQBGETqNMmCIIgCJvw/wHe1zZc2ftR1gAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(lgbm_1, plot = 'boundary')","metadata":{},"execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(blend_soft, plot = 'pr')","metadata":{},"execution_count":41,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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4MGDXHLJJRiGwZAhQ7xfFiri4osvZubMmdxwww3ea8bh4eHMmTOH6dOnU1hYSHR0NE888QSGYdC9e3cef/xx+vbtW+4AuvJMmjSJe++9l0WLFnHhhRfSrVs3b8BecsklWJbFQw89RHFxMYZhcN555zFv3rxSg+fq1avHn/70J4YNG0ZCQgK33347gwcP5k9/+hNLly5l0aJFpKSkEBISQnR0NDNnziQuLq7c1ysiOTmZXbt2MXz4cMDTW/Xr33epfQzrdL7SiogEMMuyvGE+YsQIbzCL1CTqihcRAWbNmuXtUUlNTWX37t3e0fMiNYlPg33Hjh0MHjyY119/vcy6VatWcdVVVzFy5Eiee+45X5YhIvK7brzxRvbu3UtycjJ33HEHU6ZMqdBgT5Hqxmdd8YWFhdx66620bNmSDh06lBm1efHFF/Pyyy8THx/PmDFjmDFjBm3btvVFKSIiIrWGz87YQ0JCeOmll7yTPJzswIEDREVF0ahRI2w2G/369avUB4KIiIjUVj4bFR8UFPSbtw9lZGSUmkShYcOG3luPymOapnd0dEVvAxEREanJTkxqFRERcVozCPrldrfyev9PFdgFBQWl7lUWERGpLdq3b19qxsTf45dgj4+P906gAZ75n2NjY39z++DgYM//1I8He+299f71dbv56Wgur46+gJiIsN9/w2nasmWLRgFXAR1n39Mx9j0dY99zOBzs2LHjfxlYQX5JyaZNm5Kfn8/BgwdJSEjgyy+/9D44ozzes3l7ENhP7wMGkuMuyCpxYwsKocQyOFbkpMTlxuk2aduwLiFB9j/cRmhoaCVUKr9Hx9n3dIx9T8e4apzuJWifBfuWLVuYNWsWhw4dIigoiKVLlzJw4ECaNm1KcnIy06ZN49577wU8I+RbtWrlq1ICxuq9GYQF2bn0357HaVqWhQUYwFVdW3D/4LNP+X4REQl8Pgv2s846i9dee+0315977rm88847vmo+IPVo1pC1BzJpH1uP8JAgcoudNK8fwTe708gudPi7PBERqQZq7wXrGuiuvh3LvJZf4uSb3WllXne6TQodLo7kFZGeX0y+w8WerOME2W1sPpxDVJ0QXG6T0Umt6NYkusz7RUSkZlKwB4gVqUe54a2V7MvOp8DhwrLA4n/d9Z5lixM3JFhYGBiEBdvPKNgdLjcFDhfp+cXkFTvJKCgh7XgRlgWpWceJDAli85FjxESE4nSbjElqxYVt4n9/xyIi8oco2Gu4IJsNCyh2uVl3IAsLwLJoEB5KdHgINsOgXWw9XKZJywaR2AyDuMgwXKbJo8u3lNpXvtPN3ux8DuUWcvBYISVuNz+n5xEWZGfdwSyiwkLYlnYMwzAwTavMFwfP14X/fYn4pRQMA6LCgssNdtO0KHa5ySwoIaughCKniwPHCrHbDHZk5BFss7E3J596ocGEBtm5u18n6tcJKbMfERHxULDXcGHBdib270yQzUar6EhCg2wVGkGZWVACwOJth1i87RAWUFBQSJ2V6WXO8E8+0w8NslPkcNK8QQQA0eGhNKgTgt1m0Ky+57X4umGEBtmJCgvGwODej9fy5a6jPPDpevZl55NX4qTEZZJdWAInehZO7k341RcD65eNguw2LmgVy0UdGlfmIRQRCSgK9gBwdqMGp/2eYLuBaUKR0w14zvyjQu20jKtHiN1O+1jPZAjN6kcQHhJEgzohnqA+zdsuChwuLKDEZfLfnw6cFOQQGuSZSalVdCSFDjftYutS5HTTokEElgUNI0OJCA4iItTTrb9w82/PTigiIh4K9loqKiyEOcPPpV5YMLZfwnrb9m106tipUtuJCAliXI/WmBa0i61LvdBgouqEeNusqNTM45Val4hIoFKw12JVda16YLtGf3gf6fnFADz46XrOb9GQemG6zi4iUh6fPo9dpLIE2204TROXabE1Ldff5YiIVFs6Y5caYUSXFtgMg0+3HfJ3KSIi1ZqCXWoMu81zXf5IXhHLdx7hSF4RpgVFThc9msWQ1DTGzxWKiPifgl1qjI2Hc7AseGTZZkyr9K14HeOjeGfshZimhc12egPzREQCiYJdaozzmjVkZ0YejSLqUD8shKSmMZiWxdvr97An6zh9n1lCscvN3Rd24trurf1droiIXyjYpcZI6diYlI5lJ6d5a/0eCp2eKW7tNoM5X2/ji51HcbjcNG8QwYR+nWkYWfnPrxcRqY4U7FLjvTq6NwDZhSVM/HgdDrf5y/S6Fj+l5dKlcQNGnaPHAotI7aDb3aTGsxkGNsOgYUQYiQn16do4mj+f344WDSIxTYvZX25lZ0aev8sUEakSCnYJKJMGJHJX3470ahnL6HNa4vrl3veZn2/GbZr+Lk9ExOcU7BKwOsRF0SEuCpdpsulIDj+n66xdRAKfgl0C2sT+nb23xrlMy9/liIj4nIJdAlpokJ0BbRMAeOTzzby7Ya9/CxIR8TEFuwS87/Zl4jJNtqfl8tjyn0g/XuTvkkREfEbBLgFvykVdiI0Iw2WauC2Ly17+ElPd8iISoBTsEvCaRIXz2LDuDGibgMPlxmVaXPrycp7+epu/SxMRqXQKdqk1bjyvLQAlLjf7svN5fd1ufjp6zM9ViYhULgW71CovXtOLmRefA4DDbTL+wzW6v11EAoqCXWqVsCA7jaPCubVXe1xui5wiB48s2+zvskREKo2CXWql3q3isLBwuEzS8ou9rxc5Xazem8H8taks+/kwlqVBdiJSs+ghMFJrvTKqNze/s4of9mcyeO5n5BU7sSwwLQvTArvNoElUOJ0T6vu7VBGRClOwS61m4bnWnp5fjGmBZVlEhYWQW+wg1LDzc0YeLtNi9b4MNhzKJioshIHtErioQ9nHx4qIVAcKdqm17DaDsxvVp8jh5souzekQV48gm+fq1N0Lf+B4iZMZn20CwARM08LCYn9OgYJdRKotBbvUahP7J5b7elxkGNmFJURFhHFWo/rklzjpGBfFG+v2YOq6u4hUYwp2kXJMHnw2ucUOosJCSr2+cMsBP1UkIlIxGhUv8ht+HeoiIjWBgl3kNBSUuNiddZxb3l3NitQ0f5cjIlKGgl3ktFgUu9x8ty+DT3466O9iRETKULCLnIbbe3fgssRmWBasSD1KVkGJv0sSESlFwS5yGs5vEUty+0ZYgMu0eGXNLn+XJCJSioJd5DRFhgYTEx6KyzTZk52vaWdFpFpRsIucgbv6dsSy4Pt9mYx7ayUzlm3i5e93UuR0+bs0EanldB+7yBloEhWOaVk43G62HDnGpsM52AyD1jF1GdA2wd/liUgtpjN2kTMQbLfxzJXn4TYtSlxunG4Ty4IHP13PS9/txDTVPS8i/qFgFzlDUWEhzB/Th/lj+tApLgq3ZZJf4uL/Vv1MatZxf5cnIrWUgl2kEow9tw1u08Jles7cx7z+DYPnLmPSx+v8XZqI1DIKdpFK0CQqnPlj+gDgMk2KnG6O5BWyIvWonysTkdpGwS5SiaYP6UZMeBiPXpJEu9h6/i5HRGohBbtIJWoRHcnjl3WnUb067MzIw7LgxdU7/F2WiNQiCnYRHypyuXl/4z5/lyEitYiCXcRHXry6F5ZlkVPk4D/fa+pZEakaCnYRHwkLtgNQ5HTz6bZDfq5GRGoLBbuID714TS+CbIa/yxCRWkTBLuJDYUF2XG6T/Tn5TPx4LV+nplHkMv1dlogEMM0VL+JjFlDodPPlrqMs33mUkuIi+h80GX9hJ9rrljgRqWQ+PWOfOXMmI0eOZNSoUWzatKnUujfeeIORI0cyevRoHnnkEV+WIeJXL13jGURX7HTjcJs43BYr96Tz1S5NXiMilc9nZ+xr1qxh3759vPPOO+zatYv777+f9957D4D8/HxefvllPvvsM4KCgrjpppvYsGED3bp181U5In4TGmT3zkr37LfbScvI5FCJn4sSkYDlszP21atXM3jwYADatm1LXl4e+fn5AAQHBxMcHExhYSEul4uioiKioqJ8VYpItXFnn46c3zgSgHUHs9h69BiWpSfBiUjl8VmwZ2Zm0qBBA+9yTEwMGRkZAISGhvKXv/yFwYMHM3DgQLp160arVq18VYpItZJW4MTC4of9WYx9cyXf7cv0d0kiEkB81hX/67MQy7IwDM9tP/n5+bzwwgssWbKEyMhIxo0bx/bt2+nYseMp97krNRWnnnPtU9u2b/N3CQGvWd0QCvbmAhBkM3jgw5XM7N2UYLtui6tM69bpyXq+pmNcPfks2OPj48nM/N+ZSHp6Og0bNgQgNTWVZs2aER0dDUCPHj3YsmXL7wZ72zZtwB7sq5JrvW3bt9GpYyd/lxH4tm/j3ZsG8+SKrWw8nEOuaWODqx5f/5xG2vEiil1uYiPDmDe6N8F2G4YBdYJ1A8vpWLduHd27d/d3GQFNx9j3SkpK2LJly2m/z2dd8b1792bp0qUAbN26lbi4OCIjPdcWmzRpQmpqKsXFxViWxZYtW2jZsqWvShGplv58fjtMy8K04N/f7WBb2jHS8os5VuRgf04Byf+3jAufXUrfZ5Zy5StfkVvk8HfJIlID+Ow0ICkpicTEREaNGoVhGEydOpUFCxZQt25dkpOTufnmmxk7dix2u51zzjmHHj16+KoUkWopMjSYPq3i+HZPOrihe9MYrjirGS+v2cXe7HxcNgO36bmEtTc7n/lrU/lrX/WoiMip+bR/b+LEiaWWT+5qHzVqFKNGjfJl8yLV3i292nPjeW0Jtv+v8+wfQ7rx5a6jHM0r4qquLbj5nVW4TJNCh9uPlYpITaELdyJ+dnKonzCgbYL3//95SRIPLl5f7nsty2Lj4Rw+3nIAwzBoER1Bx7goEuOjiAjVeBSR2kjBLlJDbDicjds0OZRbxHsb9vLJ1oMUOFyYloXbBLdlEmK3Y1oWF3VozKOXJvm7ZBHxAwW7SDWXW+zAsmBHRh695izGtMBtWZimhduysCyLHs1iWHsgiyLThc0wWLknnWlLNvL9/kxaNIhg4oDOtG1Yj91Zx9l85BjN6ofTIbYedYKDsOnpcyIBpUYFuznmMsjMKPWacdMdGNfe5Fn/t7/Auu/LvrFrErYnXgTAevc1rBfmlLt/Y/EqjJAQrN07sf48uvxtps/GuKCfp71rUiArq+xGV1yD7a9/82zz2DRY+knZbZo2wzbvQ09NXyzGmvlQ+e298TFGQmOs3GNYVw4uf5t77se4dISnvduuhZ0/l92o7wBs0/7lae/fz2K99WqZTVra7fCZ5/hZG9Zi3Xtb+e09/TJGYldPeynng8tVdpuxf8YYd6tnmwfuhu++Lbujzmdje+YVT3sfvo317OPlt/fxCoyICKwDe7FuuKr8babOwrhwkKe9a4fB0SNlN7rkCmwTHvRs88QMWLSw7DYJjbC98V9PTV9/gfXwfeW39+r7GM1aYhUUYF3Wr/xt7pyIMdwzjsT8642wdTMArZ0OzOAQz0bn98H2yFOe9ua9gDX/pTL76RAUhDnucUpcJi0Pp/KX9x7DbrNRJ9hOaJANA08wG7P/D6trd258exUTn7idEEcxPX/Zxy7DYBew4tyhLOk9nCCbwaiPn6fb7g00jAwr1V6dzom0f/9jALLefYuD0x4s9/MlrlxLUIMGOA4dZFty+ceg+WNP0uDSywDYPnQQJfv2ltmmwRVX0nym53fz0IxpZL75WpltgmNj6bziOwByv/ycvX+5tdz22n+4iDodOmI5nWzs3KbcbRpPup/YG/8EQOq4MeT/UPZvRt0L+tD63/MASHvheY7OmV3uvrpuTQWgcMtmdl5zRbnbtH7xFer2uRCALb264849Vmab2HE30fi+BwDYN/Fujn363zLbhLVrT4ePFgOQ/eEHHHjgb+W21/nLVQTHx+NMS2PrgAvK3abZI48RPdzzN+Pny4dSvHNHmW3qXzyMFo97fjcPz3qEjHn/KbXedDjYEhvHWas997If//Zrdt9yY7nttXt3IeFnnQ3wmz+XhPH3En/rHQDs/tM4jq8q+zcj8tyetJn3JgAZr/ybw//6Z7n7OnvDNmwhIRT9vJ0dwy8pd5uWz71A1ADP39St/c7HmZFRZpuGY66nyYPTANg/eRI5CxeU2Sa0RUs6Lv4CgJxPPmb/3+4pt71Oy1YQ0qQprpwcfupd/mDxpgzjJT8AACAASURBVNNmEHONJ3t2XHUZhenp8ORz5W57KjUq2EVqIwOYN7o3Czbvp1/rYKI/DTvlttYvZ/Ehdhsm4HKb3gmjnKaJw+3G6faMuNeETyKBx7BqwETV3pv0Y5poghof0gQ1VaMqj3Ohw8UdH3zPFWc146IOjQkPCcK0LI4VObh74Q/UCbZjMwzaxdYjyGajwOFif05+mf30bR1PYkJ9bjyvjXcGyepMk6f4no6x753IvrPOOovQ0NAKv09n7CIBLDwkiFdH9y71ms0wiA73/JEocroJstnYdDiHYLsNt2lhWhYWYDPgxNf+ZTsOsyI1jdjIUIYlNqviTyEip8Onz2MXkepr/pg+jElqReeEKEzLosTl5sqzmzPr0iRCg2z8tU9HJvTr/Muoewun2+ThpZu47b3v/F26iJyCzthFarEhHZswpGOTMq+/eHUv7//PH9OHHw5k8sw323GZJusOZHHuE4u4pHNTpg3pWpXlikgF6IxdRH7Xuc0aMn9MHwCKXG6KnC4+3XaQ19ft9nNlIvJrCnYRqbDnR/Tk0UuSsIBCp5unv97Gl7uO+rssETmJuuJFpMIiQ4OJDA3mxWt6ceu7qyl2ubnvv+vo1TKW/BIXTeuHc/sFHUioV8ffpYrUWjpjF5HTFhZkZ0pKV0wTSlwmX+9OZ+2BLD7ecpAVqWn+Lk+kVlOwi8gZaRNTl5t6tsVlmjhcbuqFBWNaFo9/9RM7MvL8XZ5IraWueBE5Y/3axNOvTTyAd+S8zQ27Mo9TPyyYOsFB1A3TpFIiVUnBLiKV4txmDUnp0Jjlu44ybckGTMszyc3ntycTbLexOyuflg0i9DhZER9TsItIpckrceJ0mzjdJuC5Fj947jKsX55IF/SrJ8k1qhfOi9ecT3xdz2A7y7L4OT2PXVnHaVS3DvuPFVDkdNOjWQztY+tV+ecRqYkU7CJSaf7csx1tG9ZjQNt4bnp71S8PnfnlwTSAwzCwG4Z32tr9OQUM+/fyUvswLXCbFnabZzvzlwfaAATZbNx1YUdGndOq6j+cSA2hYBeRShNkt5HcvhEA01K68uoPqYzv24mYiFCKnW7mr00l3+FiXI823PfJjzhNE5f5v/db4H0SHW5IqFuHtONF3jN+G25mf7mV8OAgLjtLc9aLlEfBLiI+0TqmLv8Y0s27HBZs55Ze7b3LL11zPm/8uIfO8fV5b+Ne/tqnI7GRYQTbS9+ss2T7IbIKSrikc1Pu+nANDrfJjGWbOHiskIjQIEZ2a0lYsL3KPpdIdadgFxG/MAyD67q3BiCpafRvbnfyXPZ3X9iZZ77dhsu0eGH1DoJsBs99u51G9cI5kleIZYFhwFUtw9ETRaW20n3sIlJjtIyOwG16nkTnMk0cbpMSl8ne7HyKnJ457IucbuZtzeRIXiEAecUO9ucU/K+LvxyZ+cW8u2Evb6zbTWrm8ar6OCI+oTN2EakxosNDvQ+jcbpN5q7awdoDmVzTtSV928RR4jKZ+PFaXKbF5S9/CXieKX/i+fKdE+oTERLEsMSm/LA/i5V70skuLMG08A7UO7dZQ14a2esUVYhUbwp2EamRgu027urbsczrfVrF8fm2/ZS4TG9Y2wwDm2Gw9kAWQTaD7/ZlYJqeMHdbFpZl0So6kr05BZS43X74NCKVR8EuIgHlll7tCSrKxV43mmu6tSDEbufGt1dyXvOGtGgQwTsb9uJ0Q49mMfRpFUfn+PrewXfj3lrJ9rRc9mbnU+Jy065hPWy/uvdepLpTsItIwOnVOJJOHdt4l0903wNc0rnpb7/RApdpcdWrK7CwuO2CDvz5/Ha+LFWk0inYRUR+0SA8hOzCEgDsNoN92fl+rkjk9CnYRUR+8dQV5wKwIz2Pfy7fzJLth/ns58O0bViPq7u1IKVDY8JD9GdTqjf9hoqI/ErjqDqe2+osN3abwda0Y0z/LJeZyzZjGNAqpi5nN6rPg8ld/F2qSBm6j11E5FciQ4P59zW9MC2LZvUjcLpNSlxu733zPx09xsLNBxg8dxmPf/mTv8sVKUVn7CIi5QgJspcadLczI4/pyzZxa6/2vLB6ByUuN0fzCnl3w17GJLWicVS4H6sV+R8Fu4hIBbSLrecN+s7xUew/VsDsr7bidFtc8Z8vWXjTAGIjw9iRkYfLNDk7oYFulRO/ULCLiJymBuGhNAgPpV+beL5OTccwbFz28pcYBpimBQbYDU+oD2ibwNBOTfhqVxo2A9rH1aNr42haRkdQJ1h/gqXy6bdKROQM3dyzHamZ+RzMLQDAwDN9rWEYnDhXX7bjCJ/vOOKd5S7Ebse0LM5vEcvzV/X0V+kSwBTsIiJ/wMxLzgGg2OkmNMiGBXy69RDnNI3m/kU/UuLyTFHbp1Uc3+5Jp8h0YWDww/5Mzn1iEcO7NOeKs5oRGuQJ/LYN62IY6sKXM6dgFxGpBCempTWASxM9s9udPPgO8D6Pfuyb31LodGEzDBZs2s8HG/fhyXIDmwFnN2rA45d1p0F4aBV+AgkUut1NRKSKPT+iJ/931fmYlkWR00Wxy02R0+397/pD2fyckefvMqWG0hm7iEgViwwNBkqf0ZuWxWc/H2bDoRxSs47z1wVrMICUjo25/YIOfJWahtu0uKBlLG0a1vVT5VITKNhFRKoBm2EwpGMT1h/MxuE2wQUYsHjbYRZvO+x9BO2r4aEsvXUwQXZ1uEr59JshIlKN/H3QWTwytBuvjL4A65eu+iKni+b1I3CZFseKHFzw9GKWbD+EZVmYpuefyAk6YxcRqUYMw6Bp/QgA5o3ujdNtEhLkGZg39s1vKXGZ2G0GD366gSmLN2ABIXYbkwefTeuYuoQG2cgtctK0fjixkWF+/CTiLwp2EZFqyjAMb6iD55r8+kPZPLliK27T5MSd8y67jalLNmJaFjYDTAvqhgbx5R0pmv2uFlKwi4jUIOc0iWb+mD6sSE3jnCbRLNl+iEVbD+F0uzi5Q95mGJw/51MALAs6J9SndUwkW9NyadEggjv7dKR5gwj/fAjxKQW7iEgN1K9NPADXdGtJSsfGRIWFeNd5uuw9E+OcCPvNR3LYeDgb07L4OT2Xr3Yd5Y3r+tIutl5Vly4+pmAXEanhTg518Nwn/+2edKLCQujVMtYb9Be2jsduM/hy11EAtqblKtgDkIJdRCTARIYGM6RjE+/yr2fAaxdbj1fW7AKgyOnih/1ZuC2LFg0iaB2je+RrOgW7iEgtczSvCIBHlm1ixrJNuE0Ly4Igm4FhwJ19OzK2Rxs/VylnSvexi4jUMpGhQTjdJoVON0UONyUuNw63m0KniyKnm6e/3s55Ty7itbW7/V2qnAGdsYuI1DJDOjZhcLtGHM4roln9cO/T5PJLnNzxwfcU/fKAmme+2cYz32zj3OYNaRgRitNt0jQqghvO09l8daZgFxGphYLstjK3u0WGBjN/TB9My+KGt1ZS6HRjNwy+3Z2OzWZgmhZ2m8GWo8dobitgvXMnF3dqQuOocD99CimPgl1EREqxGQYvj7yAuat+BgzWHsgkxLDjMk3Axnf7MlheWEidPYW8sHoHd/bpSExEKF0aNdC98dWAT4N95syZbNy4EcMwmDx5Ml26dPGuO3LkCBMmTMDpdNK5c2f+8Y9/+LIUERE5DcF2G3f17VTqtT3Z+UxdsgGXCU7Twu5yY9ltzPl6G5YFwfbSs9y1jI5k8uCz6dYkuipLr/V8NnhuzZo17Nu3j3feeYcZM2Ywffr0UusfffRRbrrpJt5//33sdjuHDx/2VSkiIlIJWkVHMn9MH+aP6cPDFzThyi4tcLpN7+C7E8+UL3GZFDnd/Jyexy3vrubzHUf0oJoq5LMz9tWrVzN48GAA2rZtS15eHvn5+URGRmKaJuvWreOJJ54AYOrUqb4qQ0REfOSKs5pxxVnNAM8tdN/tz2TVnnTObxFLeIidt9bvpcRl8vdPfuSabi2Y2D9Rc9dXAZ8Fe2ZmJomJid7lmJgYMjIyiIyMJDs7m8jISJ5++mnWrVvHOeecw4QJE7wjM3/LrtRUnPrW51Pbtm/zdwm1go6z7+kY+96vj3GHIOjQLhwowLIsejYM4qsDx7EZBq+u2sa81du4OymepDhdh/clnwW7ZVlllk8Et2VZpKWlMWLECO666y5uueUWVqxYQf/+/U+5z7Zt2oA92Fcl13rbtm+jU8dOv7+h/CE6zr6nY+x7FTnGnTvBVfnFTPx4LYZhYDcMXth2nMQcO/3axHNTz7ZVVG3NVFJSwpYtW077fT4L9vj4eDIzM73L6enpNGzYEIAGDRrQqFEjmjdvDkCvXr3YuXPn7wa7iIjULHGRYcwb3ZuvUtN4Zc0uTMvix4NZbD16jB8OZLLxUA5N6oczJqkVw89u7u9yA4LPBs/17t2bpUuXArB161bi4uKIjIwEICgoiGbNmrF3714AfvrpJ1q1auWrUkRExI8Mw2BA2wQeufgc+raOw7QsnKbJqj0ZHC9xsj0tl5nLNvPmj3v8XWpA8NkZe1JSEomJiYwaNQrDMJg6dSoLFiygbt26JCcnM3nyZKZOnUpJSQnt2rVj4MCBvipFRESqgWb1I7i5ZzuiwkL4+KcDjElqxdmNGnD/oh8pcbl5asVWLmgZi2lZbDiUQ0xEKH1bxWnA3WkyrF9fDK+GvNcZYproGrsP6bpk1dBx9j0dY9+rzGOcVVDChI/XEmyzYbcZgIXbArsB/3d1L3o0i6mUdmqaE9l31llnERoaWuH36SEwIiLiVzERoTSvH/HLvfCeB9E4XG7cJtzx/nfctWANLrfp7zJrDE0pKyIifjd9aLdSy6+s2cVXu47iNA1W7knn9ve/58Vrzv/d26JFZ+wiIlIN3XheW168pheWZVHiNvnxYBbnPfUp5z25iGe+0RwFp6JgFxGRaik0yM6ro3sTYrdR7HJT6HBR4HCxZPthXG6T3CKHv0uslirUFf/dd9/x2muvkZubW2rimTfeeMNnhYmIiNgMg5eu6QV4Jjcb99ZK0vOL6fX0YiwLbu7Zltt7d/BzldVLhYJ92rRp3HbbbTRu3NjX9YiIiJTrxPX1QoeLiJBgil0uXlmziz3Z+TSuV4dru7cmNjLMz1X6X4WCvUmTJlxxxRW+rkVEROSUnrnyPE50HN+98AecpsVnPx/GMAxW7s3gtTF9CAu2+7dIP6vQNfa+ffvyzjvvsGfPHg4cOOD9JyIiUpWiwkKoX8fzz7QsHC43NsPA5TbZnXmc2V/9VOsfEVuhM/b58+cD8MILL3hfMwyDL774wjdViYiI/I75Y/oAUOJy8+d3V+M0TT7cfICtabm8MuoCQoJq55l7hYJ9+fLlvq5DRETkjIQG2Zl9WQ/u/XgtJZab7Wm5bDl6jKSmtXPGugoFe3p6Ok899RSbN2/GMAy6devG3XffTXR0tK/rExER+V2xkWG8PPIC7vpwDS7TotDh9ndJflOha+xTpkwhMTGRJ554gscff5zWrVszefJkX9cmIiJSYcF2G1Fhnmvv93z0A7O+2EINeBxKpatQsBcVFXHttdfSrl072rdvzw033EBhYaGvaxMRETkt/dvG43SbFDvdvLdxHz2f+rTWzVRX4WBPT0/3Lh89ehSHQzP+iIhI9TKkYxNeuPp8TMvyzFbndDP/h93MX5vq79KqTIWusd9xxx1ceeWVxMbGYlkW2dnZPPLII76uTURE5LTVCQ5i3ujeHC9xceeC7ylyuXn2m+18seMofxuYSGJCfX+X6FMVCvb+/fvz+eefs3fvXgBatWp1Ws+GFRERqUqGYVAvLJiXrunFn99dTYnLZOPhbJ79djtzrzrf3+X51CmD/YMPPmDEiBHMmTOn3PXjx4/3SVEiIiKVITTIzsT+ibz54x6OHC9i3YEsej71KdOHdiO5faOAfAzsKa+x22ye1Xa7vdx/IiIi1V2Xxg149NIkLMuiyOmm2Olm8qIf6fnUp3y/L4OdGXkcPFbg7zIrzSnP2IcPHw7AnXfeSX5+PpGRkWRmZrJ3716SkpKqpEAREZHK8PyInqzel8lra1MxLYMgm4073v8e8HTdt4uty4Vt4rmtV/safSZfoVHx06dPZ/HixRw7doxRo0bx+uuvM23aNB+XJiIiUnkiQ4NJbt+IeaN7Ex0eisPtptjl+VficvPT0WO8/N0ujuQV+bvUP6RCwb5161auvvpqFi9ezPDhw3nqqafYt2+fr2sTERGpdIZh8MTlPZg/pg/zx/Rh9mU9aB9bj6iwECzL4oXVO3CbJkfyCnGbpr/LPW0VCvYTM/d89dVXDBw4EED3sYuISECIjQxj8uCzyS4sweE2WbT1EOfPWcyl/17O3QvX+ru801ahYG/ZsiUXX3wxBQUFdOrUiYULFxIVFeXr2kRERKpMqYltHC5KnG4O5da8WVYrdB/7I488wo4dO2jTpg0Abdu25V//+pdPCxMREalKdYKDeHV0bwpKXNQNC2b8wh/8XdIZqdB97M8++2y563Ufu4iIBBKbYVA3LNjfZfwhpwz2k+9jFxERkeqvQvex33777axfv54ePXoAsHz5cvr37+/z4kRERPzlWGEJDpebnMISGoTXnGnUKzR4burUqXz22Wfe5TVr1vDAAw/4rCgRERF/swCXaTFt6UZ/l3JaKhTse/fuZfLkyd7lv//97xw8eNBnRYmIiPhbtybRONwmq/dmsPXoMX+XU2EVCvbi4mKOHfvfh0pLS6OkpMRnRYmIiPjbhH6dwbJwuE3GvbWSDzbVjInZKnS721/+8hcuvfRSGjVqhNvtJj09Xc9jFxGRgHfbBR2Yu+pnTMtiw6FsRnRp4e+SfleFgn3AgAF8/vnn7Nq1C8MwaNOmDWFhYb6uTURExK96tYylQ1w97v245sxAV6Gu+NzcXObMmcOrr75KYmIiq1atIjs729e1iYiIyGmqULA/9NBDNGrUyDtgzuFwcN999/m0MBERETl9FQr248ePM3bsWIKDPbPxDBkyhOLiYp8WJiIiIqevQsHucDhwOp3eB89nZmZSWFjzJsYXERE5Xb884JSl2w/z+Y4j/i2mAio0eO7aa6/lqquuIiMjg9tuu43NmzdrghoREakVgu02TNPz1Le/f/IjNgMW3NifpvUj/F1auSoU7BdffDFJSUmsX7+ekJAQ/vGPfxAXF+fr2kRERPyuXlgwjw3rzqT/rqPYchNss/HtnnRGndPK36WVq0Jd8XfffTcJCQkMHTqUQYMGKdRFRKRWia9bh/lj+jCsc1MsLG/3fHVUoTP2pk2b8v7773POOecQEhLifb1Zs2Y+K0xERKS6aRHt6X7fnp6LZVnesWfVSYWC/dNPP8UwDKyTvqIYhsEXX3zhs8JERESqm92Z+ZiWxaKth0jp0JgLWlW/HuxTBnt+fj5z586lffv29OjRg3HjxnlveRMREaltBrVPYNG2g7gNk2lLN9I+th6Xn9WMZvUj6Bgf5e/ygN+5xj5t2jQsy2LkyJGkpqby/PPPV1VdIiIi1U7DiDC6No7GZVqkHS/m2z3p/O2/P3Lj2yspdLj8XR7wO8F+6NAh/va3vzFgwABmzJjB2rU1Z65cERERXxjbozWmZdEuti5Ot4nD7cbhMhk1/+tSl6z95ZRd8UFB/1ttt9t9XoyIiEh1FxsZxvwxfbzLU5duZE/WcQ7nFTH2zZX8Z9QFBNsrdNOZT5yy5V+P9quOo/9ERET86eGUrgAUOV38dPQYf12wxq/1nPKMff369fTv39+7nJWVRf/+/b1D/L/66isflyciIlL9zR/ThwcXr2d/TgHrDmTxr+U/MWlgol9qOWWwL1mypKrqEBERqdFmDD2HsW9+S5HLzXsb93J3v05+6ZI/ZbA3adKkquoQERGp8eaP6cO4N1fiNi1mLNvEw0O6VXkN/ru6LyIiEoDCQ+w43CaLtx3icG7VPwlVwS4iIlKJnr2yJxbgdFu8uHpnlbfv02CfOXMmI0eOZNSoUWzatKncbWbPns3111/vyzJERESqjN1m0KtFLG7T5NNtB9mellul7fss2NesWcO+fft45513mDFjBtOnTy+zza5du/jhhx98VYKIiIhfXNe9FaYFDrfJR1sOVGnbPgv21atXM3jwYADatm1LXl4e+fn5pbZ59NFHueeee3xVgoiIiF9EhgYz7lzPDHVmFc9GV6Gnu52JzMxMEhP/dw9fTEwMGRkZREZGArBgwQLOO++80xp5vys1Fafp/+n6Atm27dv8XUKtoOPsezrGvqdjfGpBhU6Kior5ee8B1q1zVF27vtrxr+fLPfm5tceOHWPBggW88sorpKWlVXifbdu0AbueLucr27Zvo1PHTv4uI+DpOPuejrHv6Rj/vuDMPOr8fJwteRaHw+IYltjstN5fUlLCli1bTrtdn3XFx8fHk5mZ6V1OT0+nYcOGAHz33XdkZ2dz7bXXcuedd/LTTz8xc+ZMX5UiIiJS5ZrWj8BtWTjdJv9a/hPHi51V0q7Pgr13794sXboUgK1btxIXF+fthh8yZAiffvop7777Ls8++yyJiYlMnjzZV6WIiIhUubAgO3f07oBpWeQ7XAya+xnTP9uEw+X2abs+64pPSkoiMTGRUaNGYRgGU6dOZcGCBdStW5fk5GRfNSsiIlJtnNe8Ic+v/JkSlxunYbBw834GtE2gT+s4n7Xps2AHmDhxYqnljh07ltmmadOmvPbaa74sQ0RExC9shsH8MX0ocrq49b3vMC1wmqZv2/Tp3kVERIQ6wUFcm9S6StpSsIuIiAQQBbuIiEgAUbCLiIgEEAW7iIhIAFGwi4iIVAELz4ysvn7am4JdRESkCvywPwvTsnj5+12kZh73WTsKdhERkSpwXY/WuEwT07J4Y91un81Ap2AXERGpAq2iI4kKC8FlmizccoDezywhu7Ck0ttRsIuIiFSRvw86C7dpUeJy43JbpLzwOZM+XsdHWw5UWhsKdhERkSrSJCqc+WP6MKRjExxuNyUuN0u3H+LF1TsqrQ2fzhUvIiIiZY1JakXvlrF8vTudZTsOY1pWpe1bZ+wiIiJ+0CI6kut7eOaPz8wv4ckVWytlvwp2ERERPytyuvl+X2al7EvBLiIi4kfzx/QhIrTyrowr2EVERAKIgl1ERCSAKNhFREQCiIJdREQkgCjYRUREAoiCXUREJIAo2EVERAKIgl1ERMTPCkpcpGYdr5Q54xXsIiIifmZhUex0s+5g1h/el4JdRETEz+aP6VNp+1Kwi4iIBBAFu4iISABRsIuIiFQTR/OKsf7gs9kV7CIiItWABRzKLeTpb7b/of0o2EVERKoBy7Iocbk5eKzwD+1HwS4iIlINPD+iZ6XsR8EuIiISQBTsIiIiAUTBLiIiEkAU7CIiIgFEwS4iIhJAFOwiIiIBRMEuIiISQBTsIiIi1ciK1KNk5hef8fsV7CIiItWAYRhYgNO0eGVN6hnvR8EuIiJSDUSEBNG1UQPcpkmxy33G+1Gwi4iIVBPXdm8FwOc7jlDkPLNwV7CLiIhUE063iWXB8RInH205cEb7ULCLiIhUE03rR2C3GbhNiyKn64z2oWAXERGpRh4YfPYfer+CXUREJIAo2EVERKqhAoe64kVERGo8m2FgYbFs+5Ezen9QJdcjIiIif0DT+uEMbNuICLt1Ru9XsIuIiFQjQTYb1/doTYMQGzkHd5/++31Qk9fMmTPZuHEjhmEwefJkunTp4l333Xff8cQTT2Cz2WjVqhWPPPIINpuuDIiIiPwRPkvSNWvWsG/fPt555x1mzJjB9OnTS62fMmUKTz/9NG+//TYFBQV88803vipFRESk1vBZsK9evZrBgwcD0LZtW/Ly8sjPz/euX7BgAQkJCQBER0eTk5Pjq1JERERqDZ8Fe2ZmJg0aNPAux8TEkJGR4V2OjIwEID09nVWrVtGvXz9flSIiIlJr+Owau2VZZZYNwyj1WlZWFrfddhtTpkwp9SXgt+xKTcVpntkoQamYbdu3+buEWkHH2fd0jH1Px9i3YsLsxIWHnPb7fBbs8fHxZGZmepfT09Np2LChdzk/P58///nPjB8/nj59+lRon23btAF7cKXXKh7btm+jU8dO/i4j4Ok4+56Ose/pGPvemY6K91lXfO/evVm6dCkAW7duJS4uztv9DvDoo48ybtw4dcGLiIhUIp+dsSclJZGYmMioUaMwDIOpU6eyYMEC6tatS58+fVi4cCH79u3j/fffB+DSSy9l5MiRvipHRESkVvDpfewTJ04stdyxY0fv/2/ZssWXTYuIiNRKmhFGREQkgCjYRUREAoiCXUREJIAo2EVERAKIgl1ERCSAKNhFREQCiIJdREQkgCjYRUREAoiCXUREJIAo2EVERAKIgl1ERCSAKNhFREQCiIJdREQkgCjYRUREAoiCXUREJIAo2EVERAKIgl1ERCSAKNhFREQCiIJdREQkgCjYRUREAoiCXUREJIAo2EVERAKIgl1ERCSAKNhFREQCiIJdREQkgCjYRUREAoiCXUREJIAo2EVERAKIgl1ERCSAKNhFREQCiIJdREQkgAT5u4A/zHSDZfm7ioAQZDPA7fJ3GaUZBtjs/q5CRKTGqNHBHmY6iK0XQUhwjf4Y1UbTup0IDQ3zdxmlOJwuMvIKKLaF+LsUEZEaoeYmoukmtl4EERHh/q7k/9u796go632P4++ZAUQFFURAhJaa1yCUZdZBUIvUMEstPYIBoiKoqXmsLV4T3JplYe2j5iVvKHhde9Pu4gWN8LK3V7wlUCe0RFETUbmlAzPynD8IdggMiuIww/e1los188z85jtfhM88z8Pz+5mNeyX3sLSqXwFaVs/lfK3suQshxAMw3XPsiiJ76g2ElaWFnG4RQogHZLrBLoQQQohKZJf3Mdi981ui5s4h8fv92NnZ1Xqcr//5JSuXL8fVzQ1FUVCrVcycM5f2T3eo9ZjTpkzms2XLq9z2yeIPeSsohDaurrUev0xBFXo/ZQAADwZJREFUQQFzZkRSWFhA4yZNWLT4Y5o3b1G+/dDBA2zasKH89k8/ppPw9besX7uG8xkZAGi1d7G1bcaKL9Y8cj1CCNFQSbA/Brt37cLV1Y2kfXsZPiLgkcYa4O/PtL9MB+DkiRN8/OGHrFq7rtbjVRfqANNnzKr1uPfbEh9Hj549CR0zlh3bthK7bh1T332vfHvvPn3p3acvAJcuZfK3mBhaOToyY/ac8sesXrmC9u2ffmw1CSFEQyTB/ojy8nJJSz1H9IKFbNqwgeEjAkhOSuLA/mSiFywEIGrObPz696egoIBN69fj7OKCo6Mjnt26MXjoG9WO7eHpyaVLmQAMGTQQ3959sLO3Z8jQN/hr1DyKdcVo1Brenz+f1q1d+Pabr9m2OR61Wk3QqFBe8R+IX28fvj/0b779+iu2b92CpaUlHTt1Ztbc9wkfM5oZs+fg5OxM9Nw55OXlUlJSwvSZs+n6zDMMftWfl/xe5szp09ja2rJ0xUrWr13DsSNHKtQ5a+5cjh89StSCBQC8+JIf/zNlcrXva/WKFURMfLvCffl5eRw/epSICRNr9X0QQghRyqyCPaNbpyrvbznlXezHTQDgyoQx3Dny70qPafzcC7iuiwPg9sZ15Hy6mI5nf67xNfclJtKn74v08vFlQXQU2dev4+3jw2cxn1BSUoKiKJw+dYo5UdEMHujP5u07aNKkCf/95lA8u3UzOPahA/vx8HgWAL1eTy9fX3x8ezN/3vsEjwrlBW9v/nXwIGtXr+Ld6TP4YuUKdiT8E11xMfPmzOYV/4HlY22KjWXpihU4O7fmqy+/RKvVlm/bGh/Hs56eBAYF8cuFCyz5eDFrYzdyJSuLQa8PZtpfpjMqaCQZP/8f4yLGMy5ifKVab97Mwc7OvrTfDg7k5Nyo8j3dyM7m1s2bdOnatcL9Cf/4O4OHvoFKpaqx50IIIapnVsFuDLt37iRiwkQ0Gg39+g9gb+IegkeF0rlrF1LPnUOv1/OspyeFBQXY2NrQ0sEBgJ7Pv1DleHv37CE9LQ1FUXBo1YrpM/9zuNzj2dKQP3vmDJkXf2XtF6u5V3IPOzt7fv3lAu3at8fa2hpra+tKh+D9X32V96ZO5dXXXsN/4KtYW//nevX09DTCwkvD+hl3DzIv/gpAUxsbOnXuDICTkxOFBYXV9kH501+tK4pSbUB/8/VXDBw0qNL9e3btJDZ+S7XjCyGEeDBmFewPsofdZtWGGh9jFxqGXWhYjY/77bdrpKWe49OYj1GpVGi1WmxtbQkeFYrfy/04eGA/uuJiXh4woFLYqdVVB9+fz7Hfz9LS8o+vFixe8hmtWrUq35aeloZSUv0lYWPHhTNw0CCS9u5l/LixrI3dVL5NhapCMJex0FS8blxBYe0Xq6s8FO/o6MTNnBxsbW3Jzs7GwaEVVfnXwQN8+HFMhfsuXcqkRQu7Ch82hBBC1I5c7vYI9uzaxYjAkWz/x5ds+3sCX36zk7y8PC5fvkTvvi9y6mQKJ1NO0MvHl+YtWpCXm0t+Xh5arZaUEydq/boez3qy//skAI4fO8rund/Srl07Ll78lTt3fqeoqIiJ4ePKw7qkpITlS/8XB4dWBIeOxrNbd65dvVo+nruHByknjgPww9mzPN2hY7WvPS5iPGs2xFb41/7pDvxXr158tzcRgO/37aWXj0+Vz8/KysLJ2bnCfWnnzpUfGRBCCPFoJNgfQeLuXRX++E2lUvH64CEk7t6NjY0NzZo1o42rG9bW1lhYWDBu/ATCRo9izoxInnF3R6Op3QGTCW9PIvn7JMJCR/HFypV4dutO4yZNmDhpMhPDwwkfO5qhb75ZfoRArVbTtEkTRgcHMX7cWFQq6NylS/l4I4ND+DE9jckTJ7Dsb59VOPz/oEYGBZGelsbY0BBOnDjOqDFjgdJL6q5kZQGQm5uLjY1tpefm5ORgb29fm1YIIYS4j0qp6hhsPVNUVERqaiq0bAOa0sPR3NPztINtvZsC1ZDv9ibS84UXaN68BW+PD2f8xEl0697d2GWV02rvYm3d2NhlVKIrLuZCTgHU8oNQffPjTz/StUvXmh8oak16XPekx3XPzkrN7axf8PDwoFGjRg/8PPP4TWki7t69y/iwsTRu3JhOnbvUq1AXQghhHiTYn6DXhwzl9SFDjV2GEEIIMybn2IUQQggzYrrBrlJRrNMbuwrxBBTr9CAT1wghxAMx3UPxag038n8HkOVbHxO9Toeunq15XqzTl36f1abzR5JCCGFMJp2IWrUVl/O1slb3Y5Jx4TwdH2EluTqhUkmoCyHEQ6jTYF+0aBFnz55FpVIxe/ZsPD09y7cdPnyYTz/9FI1GQ58+fZg0aVLtXqSe7WGaMn2JYjaXlAkhRENVZ+fYjx8/TmZmJtu3b2fhwoUs+GPlrzILFy5k2bJlbN26lUOHDnH+/Pm6KkUIIYRoMOos2I8cOUK/fv0A6NChA/n5+RQWli4icvnyZZo3b07r1q1Rq9X07duXI/fNPy6EEEKIh1dnx11zcnJwd3cvv92yZUtu3LiBjY0NN27cqDCFqIODA5cvX652rLLJ8axUoFbL+fS6YmOhxlr6W+ekz3VPelz3pMd1T62UXvn1sBPE1lmw31/In1c3q6pIQ+tw63Q6AIpzrjzGCsX93Jo1QvtbprHLMHvS57onPa570uO6p/3jq06ne6jVL+ss2J2cnMjJySm/XbqUp0OV265fv15hCdL7NW3alE6dOmFpaWnwA4AQQghhLhRFQafT0bRp04d6Xp0Fu4+PD8uWLSMwMJD09HQcHR2xsbEBwNXVlcLCQrKysnB2diY5OZmYmJhqx1Kr1djaVl4VTAghhDBnD7OnXqZOV3eLiYkhJSUFlUpFVFQU6enp2Nra0r9/f06cOFEe5gMGDCAsLKyuyhBCCCEaDJNYtlUIIYQQD8Z054oXQgghRCUS7EIIIYQZqZfBvmjRIgICAggMDOSHH36osO3w4cMMHz6cgIAAPv/8cyNVaPoM9fjo0aOMGDGCwMBAZs2aRUlJiZGqNG2GelxmyZIlhISEPOHKzIehHl+7do2RI0cyfPhw5s2bZ6QKzYOhPm/evJmAgABGjhzJBx98YKQKTd/PP/9Mv379iI+Pr7TtoXNPqWeOHTumREREKIqiKBkZGcrw4cMrbB84cKBy9epV5d69e0pAQICSkZFhjDJNWk097t+/v3Lt2jVFURRlypQpyv79+594jaauph6X3R8QEKAEBwc/6fLMQk09fuedd5S9e/cqiqIo0dHRypUrV554jebAUJ8LCgqUl156SdHpdIqiKMqYMWOU06dPG6VOU/b7778rwcHByty5c5W4uLhK2x829+rdHrtMRVv3DPUYICEhAWdnZwDs7e25ffu2Ueo0ZTX1GOCjjz5i2rRpxijPLBjqcUlJCSdPnsTPzw+AqKgoXFxcjFarKTPUZ0tLSywtLblz5w56vZ67d+/SvHlzY5ZrkqysrFizZg2Ojo6VttUm9+pdsOfk5GBnZ1d+u2wqWqDKqWjLtokHZ6jHQPl8A9nZ2Rw+fJi+ffs+8RpNXU09TkhI4Pnnn6dNmzbGKM8sGOrxrVu3sLGxYenSpQQHB7NkyZKHnpZTlDLU50aNGjFp0iT69euHn58f3bt3p127dsYq1WRZWFhUe716bXKv3gX7/T98yiNMRSuqZqjHZW7evMmECROYN29ehR9q8WAM9Tg3N5eEhATGjBljjNLMRk2/K65fv86wYcPYuHEj6enpHDhwwBhlmjxDfS4sLGT16tXs2bOH7777jjNnzvDTTz8Zo0yzVZvcq3fB/jinohVVM9RjKP1hDQ8PZ+rUqfj6+hqjRJNnqMdHjx7l1q1bBAUFMXnyZNLS0li0aJGxSjVZhnpsZ2dH69ateeqpp9BoNHh7e5ORkWGsUk2aoT5fuHABNzc37O3tsbKy4rnnniM1NdVYpZql2uRevQt2Hx8fEhMTAQxORavX60lOTsbHx8eY5ZokQz2G0nO/oaGhcgj+ERjqsb+/P7t27WLHjh0sX74cd3d3Zs+ebcxyTZKhHltYWODm5sbFixcBSEtLk0PEtWSoz23atOHChQtotVoURSE1NZW2bdsasVrzU5vcq5czz8lUtHWvuh77+vrSs2dPvLy8yh/72muvERAQYMRqTZOh/8dlsrKymDVrFnFxcUas1HQZ6nFmZiZRUVEUFRXRsWNHoqOjUavr3b6MSTDU523btpGQkIBGo8HLy4vIyEhjl2tyUlNTWbx4MVeuXMHCwgInJyf8/PxwdXWtVe7Vy2AXQgghRO3Ix1chhBDCjEiwCyGEEGZEgl0IIYQwIxLsQgghhBmRYBdCCCHMiIWxCxBCPBlZWVn4+/uXX8qoKAp6vZ733nuPnj17PpbXmDlzJj169MDb25u33nqLgwcPPpZxhRAPToJdiAbE3t6+wjXz58+fZ/To0Rw6dEimZxbCTEiwC9GAdejQgaKiIm7fvk1sbCynTp1CpVLh4eFBZGQkKpWKFStWkJSUhFqtZsiQIQQHB5OSkkJMTAxWVlZotVqioqJwd3c39tsRQiDn2IVo0JKSkrC3t+fYsWNcv36d+Ph44uLiuHTpEsnJyaSkpLB//3527NjB5s2bSU5OJj8/n9zcXKKjo9m0aROjRo1i9erVxn4rQog/yB67EA3IrVu3CAkJAeDq1au4uLiwatUqNm7cyJkzZ8q3FRQUkJWVhU6no0ePHmg0GjQaDevWrQNKl4785JNPKCoqIj8/X9bgFqIekWAXogH58zn2xMRE4uLiaNu2LVZWVowYMaLSHNTr16+vctnIyMhI5s+fj7e3N8nJyaxfv/6J1C+EqJkciheigXrllVdo1qwZ8fHx9OjRg3379qHX6wFYvnw5Fy9exMvLiyNHjqDT6dDr9YSEhJCdnU1OTg5PPfUUJSUl7Nmzh+LiYiO/GyFEGdljF6IBi4qKYtiwYWzZsgUvLy8CAwNRq9W4u7vj5uZG27ZtGTBgAEFBQQAMGjQIR0dHwsPDiYiIwMXFhbCwMCIjI4mNjTXumxFCALK6mxBCCGFW5FC8EEIIYUYk2IUQQggzIsEuhBBCmBEJdiGEEMKMSLALIYQQZkSCXQghhDAjEuxCCCGEGZFgF0IIIczI/wM65CLLj/u5uwAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(lgbm_1, plot = 'pr')","metadata":{},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(blend_soft, plot = 'class_report')","metadata":{},"execution_count":43,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 2 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(lgbm_1, plot = 'class_report')","metadata":{},"execution_count":44,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 2 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8OHDdq/To0cPFi1axIoVKxg8eDAfffQR77zzDh9//DFqtZpnn30WV1dXevfuzcSJE1m0aBEqlYqGDRvaDgDu7u60adOGtWvX0rRpUz766CPmzZvHtGnT0Gg0hISEsHz5cruzvjFjxvDSSy9RsWJFIiIi6N+/PxMnTmTlypUEBgbSo0cPtFotrq6uTJkyhdq1a9+yvDhCQkJ4+eWXbU85eHt78+677xZr2xdffNE2jhaLBY1GQ1hYGO+8806henXr1qVdu3aEhYXh7+/PhAkTOHLkCAMHDuSbb75h0qRJdOnSBY1GQ7Vq1WxPrNyq/OzZs0XG9cwzzxATE0NoaCjOzs6MHz+egIAAhg8fzvTp0/nkk09sT0Z89NFH1K9fn06dOvHhhx8SGxtb6NLW1KlTeeutt1izZg1arZbp06fj7+9/x3sXiqLRaBgzZgwzZsygW7dudO7cmejoaNtsV9WqVXnvvfds9WvVqkW/fv1ISUlhyJAh1KpVC4Bp06YxYsQIzGYzAQEBTJs27Zav17RpU2bPns2jjz7Kr7/+ikaj+cexC1HaVIqktkIIYScsLIwPPvjA9nisEA8aucQghBBCCDuSIAghhBDlxLlz5+jUqdMtv4xu79699OvXj/79+9vuM7oTuQdBCCFuYceOHWUdghB/i9FoZNq0abd9Im369OksXrwYPz8/Bg4cSNeuXalZs+Zt25MZBCGEEKIccHR0ZNGiRfj6+tqti42Nxd3dHX9/f9RqNe3bt2ffvn13bO+eziBYrVYMBgNarbbYj3oJIYQQZeXPLyjT6XS3fPT6XjObzba/9/F3aTSaWz5O+ycHB4fbrk9KSir0JWA+Pj62R3Vv294/ivI2DAaD7Q/sCCGEEPeL2rVr230vyr1mNps59NseHN31RVe+BY1GQ4MGDe6YJNzOrR5YLOpE/p4mCFqtFoBaSV/haM28l02LEqDq+CWkry7rMERxeDyBcmlm0fXEv4IqaAIQWWQ9Ufby8mpz7tw52/GrJFksFhzd9ex5/l1yE1P+1rbOvt60WTwFi8XyjxIEPz8/kpOTbcsJCQlUrFjxjtvc0wThz2zE0ZqJkyW9iNqirKmcnECbX3RFUfacnFDUxrKOQhST6hZ/ZEv8WxV8QVxpXhbPTUwhJz656Ir3UGBgINnZ2Vy9epVKlSqxc+dOZs+efcdt5CkGIYQQohyIjIxk1qxZXLt2DQcHB7Zu3UpYWBiBgYF07tyZd955h1dffRWAxx57jKCgoDu2JwmCEEIIUQ40aNCAZcuW3XZ98+bNC/0xsqLIY45CCCGEsCMJghBCCCHsSIIghBBCCDuSIAghhBDCjiQIQgghhLAjCYIQQggh7EiCIIQQQgg7kiAIIYQQwo4kCEIIIYSwIwmCEEIIIexIgiCEEEIIO5IgCCGEEMKOJAhCCCGEsCMJghBCCCHsSIIghBBCCDsOZR1AadtxPJnXl0RhMFmo6uPCV2MaEujjUqjO6r3xTP/hPLl5FnwqOPLZiIdoUK0C+WYrYxadYueJZKwKhD7szfwXG6B1UHPyciajP48kId2Eg0bNOwNr80Rr/zLqZfmx47coXpvyAwaDiaqB3iyZ/zyBAV6F6mzYfJTJM9dgMpnx9tKzcM5QGtQLRFEUJk5dydqfjqBSQZ/uTXl/8pMAXLmawvOvfEXM1WT0OmfmTI0g9NF6ZdHFcmPHvhhe/2AnBmM+VStX4Kv3HyOwkluhOht+Oc+UeXsw5Znx9nDhs3e70KB2Rb5df4pRU7fjX1Fvqzvj1Xb06VybK3GZDHtzMzFxmehdtcweH0roI9VKu3sPNK22JTVqBNqWW7QI4ZtvppZhRKI0PFAzCIZcMwNmH2HR6Iac+SyULk0qMuKzk4XqXEnKYfinJ1n3ZjNOfxZKvzb+PD/vOACz114kKcNE5CcdOD6vHScuZbJo2xUAnpx1mLG9gzn9WSjf/KcRz3x8jNSsvFLvY3liMJiIeOEzvvz4Wc4enEnXsAYMf31poTrX4tIYOnIR333+Mqf3v8/AJx7hpXFfA/DD2gPs2nOGE79P49ivU/n51yhWbzgEwIv/WUL3Lg9z7uAsFs4eyoAXPyMnR8brnzIY8xgwbiOLpodzZusLdGkbxIh3thWqcy0hi2cmbGL5nB5EbR7GgB71eXlyQZ30LBP9u9fj9JZhtp8+nWsD8NLbW3msfQ3Obn2Bz97tysBXfyQnN7/U+/igys42olarOXNmte1HkoMHQ5EJgsFgYNSoUQwePJiIiAh+//330oirROw4kUKwnytNargD8HJ4NbYeTSLLaLbV0WpULH+1MdV8XQHo2NCHs9cMALRv4MX7Q+qh0ahwdtTQup4XZ69lk2+28s6A2vRu6QdA4xruOGvVxCTmlHIPy5cdv0cRXK0iTRpWB+DlZ0LZuiOSrKwb76tWq+H7RS9Tv24AAG0fqc2pM9cAWLn+EM8MaIuTkxZnZ0eeHdiW/60/REamkR2/n+aFwR0AaNE0mKoB3uzac6ZU+1ee7Nh/heAq7jQJqQTAyxGN2Lr7ElnZJlsdrYOa7+b0pH5NHwDaNg3gVHQyABlZJjzcnOzazcgyseNADC889TAALR72p6q/G7sOxJZ0l8T/y8jIxsPDreiKotwpMkFYu3YtQUFBLFu2jLlz5/Lee++VRlwl4ty1bGr4u9qW9S4OeLs5Eh1vsJX5eznTuXFFAMwWK1//ctV24G9dz4ualXUAxKfmsuVwIj2a+aF1UBPRLgCVSgXAuv3X8dRrqV/1xnSp+PvOXbhOjSBf27Je74y3l57oS4m2Mt+KFQjv+LBtefPPJ2jZtMb/b59Ajeo3tq8R5MuZ8/FEX0ykorcbOp2T3Trxz5y7nEqNKh62Zb3OEW8PF6KvpNvKfL11hLcLti1v/u0SLRsWXIZLz8xl75FrtHpqGfXCv+TVmTsw5ZmJjkmjoqcrOldH23Y1qnpw5lJKKfRKAKSnZ2GxWOjZ8z/UrfsE4eGjOX36UlmHJUpBkfcgeHp6cvbsWQAyMzPx9PQs8aBKitFkwUmrKVTm4qjBYLLY1Z274SLTfjhPTX8dayc1K7Su/YS9HIpOZ9zjwXRq5GMr33cmjadmHUZRFL5/vYnda4m/x5iTh7OTtlCZi7MjBqPplvV/+TWK/y7cyo614/9/exPOzlq7bf9abltnuHW7omjGHDNOToU/TlycHDAYb30p4Jd9MXy89A9+WdofgIZ1ffH2dGHMkKbkmiz0Hr6GWV8cIPSRqjj/pV1nJ+1t2xX3nqurM08+2Ylx456mRo1A5s79nt69XyUq6n84ODxwt7E9UIqcQejevTtxcXF07tyZQYMGMX78+NKIq0TonB0w5RdOBowmC3pn+wP5mF7BJH3bhTE9g2jzxh5ybkoifp3ZmuvfdOZMbDYTlt6Ylm5V15PYJZ34cXILBnx4hOOXMkuuMw8AnasTuabCBwJjjgm9zn4qet1Ph3lm1Jf8+N1/bJcbdK5O5N50rfrPbf9ablunt29XFI/OVYvJZC5UZsw1o9dp7equ+/k8z07YxMaFfW2XGwb1DmHCi4/g4qzF092Zsc8046ddF9C5aMn9S7s5ufnob5pRECUrKCiAzz6bSK1aVVGr1YwdO5CEhBTOnbtS1qGJElZkgrB+/XoqV67M9u3bWbp0KdOmTSuNuEpE3UAdZ67euJyQlGEiLTufWv9/2QDgdGwWPx9LAkClUjGgfQCZOWbOXstm/f7rXEkquP5dwVXL0I5V2HY0idSsPJbvumpro1GwO4/U8WTnieRS6ln5VLeWf6Fp/6TkTNLSjdQKrlSo3s+7TjFm0ndsW/UazRoH3Xb7U2euUb9OADWD/EhOzSY9w3DTujjq1wkowd6Ub3WDvThzKdW2nJRqJC0jl1rVCs84/rz3MmPf+4WtXz1Fs4duPOVzMTadjKwbMziKoqB10FCzmifJaTmkZ+ba1p2KTqZ+Te8S7I24WXp6Fhcv3vh8U6lUWK0KWq3MHpR3RSYIR44coW3btgDUrVuXhIQEzGZzEVv9O4U+5MPV5Bx2RxV8kM3/8TI9mvuic77xi56UkcfQj48Rl1LwgbQnKpV8s0JwJVfWH0jgne/OYbUqKIrCT38k8HB1N7QaNaM/P8WO4wUJQWK6iQPn0nm4eoXS72Q5Etq2Hlfj0ti9/xwA8xf9TI8uDQvdO2A0mnh29GLWLB1NvTqVC23/1OMtWLz8NwwGE9nZuXy1/HcG9G1JhQoudO4QwoIvfwEKHqVMSc2mfes6pde5cia0ZVWuXs9i9x8FB5L5yw7TI7RGoXsHjDn5PDdxM6vnP069GoUP8NM/3cf4D3ehKAq5JjMLvz/GYx2CqaB3onObaixYfhQoeJQyJS2H9i2qlF7nHnAnTpynQ4eXSEgouO9j0aK1BAb6EhwsCXV5V2QKWK1aNY4fP07Xrl25du0aOp3uvr3u5OKkYcUbTRi18CSGXAs1/XUsGduQg+fSmLz8HFvebUm7Bt5M7FeTzpP3Y7UqOGnVfP96Yyq4apn9XD1GLYyk/shdWK0KIVXdWDjiYdxcHVg9sSnjvz5Ndq4Fi1VhVI/qhDX0KToocVsuLo788OVwRr6xDIPRRM0gX77+ZBgHD1/k7ffXsHXVa6zffJSklEyefmlhoW1/3TiRfr2ac/jYZRp1mIxKBQOfeISe4Y0BWDh7KENHLmLJ97up4ObCqq9H4uRkPx0uisfFWcuK//Zi1NTtGHLyqVnVkyUzu3HwRDyT5/7OlsVPsf6X8ySlGhn02o+Ftt317QBmj+/Ai29vpU7XRahVKrq1D+bV55oD8Nm7XXlm/E98veYkFfSOrJz3OE6O9+dn0P2oXbsmjBv3NI8++gJqtYrKlSuyZs2HaDRyj1V5p1IURblTBYPBwKRJk0hJScFsNjNmzBhatWp1y7omk4nIyEhCEj7GyZJ+yzri30PVcyOkLi26oih7XkNRzt2/9/88aFS1ZwGHyzoMUQwmUwMiIyNp0KABTk4lex/Sn8fIX3q+Qk7837sE7eLvQ8eN80olzj8VmYbrdDrmzp1bGrEIIYQQ4l/igfomRSGEEEIUjyQIQgghhLAjCYIQQggh7EiCIIQQQgg78qyQEEIIUYqaA3d8fPAWVCURSBFkBkEIIYQQdiRBEEIIIYQdSRCEEEIIYUcSBCGEEELYkQRBCCGEEHYkQRBCCCGEHUkQhBBCCGFHEgQhhBBC2JEEQQghhBB2JEEQQgghhB1JEIQQQghhRxIEIYQQQtiRBEEIIYQQdiRBEEIIIYQdSRCEEEIIYcehJBpVdfwSlZNTSTQt7jWvoWUdgSgmVe1ZZR2C+FualnUAolhMZR3Av5bMIAghhBDCTonMIFhe6IIlI7kkmhb3kGb9KawfPVrWYYhiUI/7XcbqPqIe9zukLi3rMERx6CLKOoJ/LZlBEEIIIYQdSRCEEEIIYUcSBCGEEELYkQRBCCGEEHYkQRBCCCGEnRJ5ikEIIYQQpW/GjBkcP34clUrFpEmTePjhh23rli9fzoYNG1Cr1TRo0IA333zzjm1JgiCEEEKUAwcPHiQmJoYffviB6OhoJk6cyMqVKwHIzs5m8eLFbNu2DQcHB5577jmOHTtGo0aNbtueXGIQQgghyoF9+/bRqVMnAGrWrElmZibZ2dkAaLVatFotRqMRs9lMTk4O7u7ud2xPEgQhhBCiHEhOTsbT09O27O3tTVJSEgBOTk6MHDmSTp06ERYWRqNGjQgKCrpje5IgCCGEEOWAoih2yyqVCii4xPD555+zZcsWfv75Z44dO8aZM2fu2J4kCEIIIUQ54OfnR3LyjT9zkJiYiI+PDwAXLlygSpUqeHl54ejoSLNmzYiMjLxje5IgCCGEEOVAmzZt2Lp1KwBRUVH4+vqi1+sBCAgI4MKFC+Tm5qIoCpGRkVSvXv2O7clTDEIIIUQ50KRJE0JCQoiIiEClUjFlyhTWrFmDm5sbnTt35vnnn2fIkCFoNBoaN25Ms2bN7tieJAhCCCFEOfHaa68VWq5bt67t/xEREUREFP+vV8olBiGEEELYkQRBCCGEEHbkEoMQQghRioL9ctGojH9rG4tvLslFV7unZAZBCCGEEHYeuBmEHUkGxkclYDBbqeKiZXHjygS6aAvVcd4YRQ2do225uYe03tMAACAASURBVIcLXzcJINtsZczJePal5pCnKLxbpyJPV/EA4HSWiZEn4kk0mXFQqZhSpyJ9Klco1b6VRzvOp/PGxksY8qxU8XDiq4haBHo43bLuT1Gp9FocxYU3m1Hdy5lsk4VRay5wICYLjRrC63oyq0cQGrWKk/EGXllzgYSsfBw0KqZ0rcoTD/uUcu/Kl+KMldPre6jh7Wxbbl5Vz9KBdci3WBm77iI7z2dgVRRCa3kwr08wWo2a0wlGRq6OLhgrdcFY9ZWxKnFx8WkMHfklZ87HU8HNmc9mD+XRVrWZOHUla386gkoFfbo35f3JT5Z1qKKEPFAJgsFs5enDV/npkao08XBhTnQyI0/Es75lVVudbLMVtUrFqbCadttPP5eEwaIQGVaDuFwzrX67RGsvV4J0jkT8cZX/1PDmmaoenMzMpe3vlwirqMNdqynNLpYrBpOFgd+eZdMLITQJ1DNn51VGrI5mw/MhdnWNeRYm/XQZL9cbv9Izf4klz2zl1BtNMFsVun4eyZKDCQx7pBJPLT3D+92r8/hD3hy9mk37BScIremOl6vWrm1RtOKMVbbJgloFUROa2m0/Z9c1ErPzOflGE/ItCh0/O8mX+xMY3saf/t+c4T/tA3i2hR8n4w20mXecjrU8cHd5oD6+St3QkV/SrdNDbF/zOjt+i+KTL38m7noau/ac4cTv01AUhUe7v0+zDYd4olfzsg5XlIAiLzGsXLmSwYMH234aN25cGnGViB3JBoJ0jjTxcAHgpepebEvMJstssdXJyLfg4XDrt+WXJANDqrijVqkIdNHS29+NDdezsCgKk2r7MCiw4A9fPFTBGSe1mkvG/JLvVDm2IzqdYC9nmgQWfNHHS6392XY2naxcs13dd7deYVBTX9ycbiRkJ+ONtK/pjlqtwtFBTeugCpy6biTfYmVK16r0buAFQONAPc4OamJSTaXTsXKoOGOVkWvG4zYH9XbB7rzfvToatQpnrZrW1StwNsmIxaowqVMVBjf1BeAhfx1ODmoupeaWfKceYLHXUjh8/DKjXyj4wz9h7erzv69GsnL9IZ4Z0BYnJy3Ozo48O7At/1t/qIyjFSWlyAThySefZNmyZSxbtozRo0fz+OOPl0ZcJeJ8dh41bjpD1Duo8XZ0INqQZytLz7dgAXofuELIjmge2xfD6ayCA4cKsNz0Vdc6jZoLhjw0KhX9A9xxUBd85/WBNCMKCrVvukwh/r7zSTkE3zQdrXfS4O3qQHRy4YPDyXgDP59LZ2z7yoXKO9ZyZ93JFHLyLWTkmPn5XDqdanug1aiJaFzR9h3l606m4OnqQP1KriXfqXKqOGOVnmPGYlXotfgU9WceptsXkZxOKLhRq3VQBWr6FCTu8Zl5bDmTRvd6XmjUKiIaV8RB8//7VkwWigK1K7qUYu8ePMcjYwmq5sOEqSup02IC7Xu+z9ETMZy7kECN6r62ejWCfDlzPr4MIxUl6W/dpLhgwQJGjBhRUrGUOKPFirOmcJddNCoM5htHfVeNmn7+FZgd4sfJ0Bp08dXT92AsZqtCx4o6PruUSq7FyhVjPuuvZ5FrLfzHMWJz8hl8+BpzH/LH9TYzEaJ4jPlWnLV/GS+tGkPejRkfRVEYsSqauf9/vfpmI9pUxmxR8Jt8gEpTDlDD25nH6t34S2f7LmdSdepBRq+5wJf9a+Ek4/WPFWesXLUa+jX0YU6vYCLfaELXOp70+SoK801Zd4cFJ6j53h88/pA3nWp7FGovNs3EoOVnmdsnGFdHuXRXktIzjJyMukq7VnU4e3Amg55sRd+h8zHmmHB2vnGS5eLsiMEoM2/lVbE/EU+cOIG/vz8VK1YsyXhKlM5BTa7FWqjMaLGiv+nAEKRzZEFDf2rpnVCrVIwJ9iLBZOacIY+3alckwEVL410XGXEinnBfPe43fSiezTbRcc9lxtfyYWDgnf/OtiiazlFDbv5fxivfiv6mywhf7L9OPT9X2gbbv9/jf7xEdS9nUqY/Qsr0RzDkWZm985ptfavqFbgyuQUbh9Vn4LKzHI/LLrnOlHPFGasgb2c+7VeTWhVdUKtVjGlXmYTsfM4l5djq7Br5MPHvtuB0gpGJP122lZ9NNBL22UnGhwXydFNfRMlyr+CCX0V3ej/WBIBhg9uTmmYg9loqubk3Lp0ac0zodbe+aVjc/4qdIKxatYo+ffqUZCwlro7ekbPZNy4nJJnMpOVbqXXTpYD0fAsXb7rkoFKpsCqgVRUkGIsaVeZ0x5r8+EhVss1WHnIrmFa9lpPPY/uuMKO+H89Xu3GWKv65Or4unE268axwUnY+aUYztXxuTC9viExlw6lUKr9zgMrvHCA23UTLj4+xMzqd7efSeaqRD1qNGldHDT1DvPj1YgapxnyWH060tdEoQE/Lam7sis4o1f6VJ8UZq/QcMxdTblxysO1bGhXrI1O4klawroKzA0Ob+7LtbDoA1zJMdPviFDO6V2fYI5VKqUcPtqBqFcnKzsFqLUj6VCoVarWK7p0bFrqkcOrMNerXCSirMEUJK3aCcODAgfv6BkWAUB8dV3Py2Z1S8EH2yaVUuvvp0d00g3AiM5eOey+T8P83V30Zk0agiwPBOkc+OJ/Ma5HXAYjKMvFLcja9/N0AGHkinjE1vOgnjzbeM6E13bmansfuiwUH7k92x9G9vhe6m85Kf3ohhOvvtiTunYKfKh5OHBjbiNCaHtSu6MJPp1MBsFgVtp5No0ElV7RqFa+svcCO8wUHoMSsPA5eyeIhf13pd7KcKM5YnYgzEPbpSRKyChLwRfuvE+juSLC3MxsiU3h36xWsVgVFUdh0Oo2H/AvuCRmx6gJj21XmyYbyaGNpaVAvkBpBvny57DcAVq4/iIe7K08/2YrFy3/DYDCRnZ3LV8t/Z0DflmUcrSgpxXpOKCEhAZ1Oh6Pj/X3TnYtGzXfNAnnlZDwGs5UaOke+ahzAwbQcppxJZHOrarTz1jE22JsOey6jVoG/swMrm1dBo1IxtIoHAw9fpdbP53HRqFnSOAAPrYa43Hx+TMjmbHYen19Os73ezPp+9KzkVoY9vr+5aDV8P7gOo9dcwJBnpYaPM0sianPwShaTN8ew5aUGd9z+v72DGbE6mjrv/wFA8ypuTOpUBTdnB1Y9U48JP14m22TBYlUY1bYyYbU87tieuL3ijFW7Gu78p31l2n9yArVKRWV3R1Y9Uw+NWsWHvYIYtfoCIR8cwaoo1PdzZeGTNYnLMPFjVCpnE40s3Hvd9nqzelanZ4h3Gfa4/Fv99Sj6P/8ZM+f+hK+PG6uWjKJpo+ocPnaZRh0mo1LBwCceoWf4/X3iKG5PpSiKUlSlyMhIPv74Y7788ss71jOZTERGRlJv7jicMkr7SyHF36VZfwrrR4+WdRiiGNTjfpexuo+ox/0OqUvLOgxRDCZdBJGRkTRo0AAnp5K9n+LPY6TPsBfRJCYWvcFNLL6+JH/5RanE+adiXWJo0KBBkcmBEEIIIcoPea5LCCGEEHYkQRBCCCGEHUkQhBBCCGFHEgQhhBBC2JEEQQghhBB2JEEQQgghhB1JEIQQQghhRxIEIYQQQtiRBEEIIYQQdiRBEEIIIYQdSRCEEEIIYUcSBCGEEELYkQRBCCGEEHYkQRBCCCGEHUkQhBBCCGFHEgQhhBBC2JEEQQghhBB2JEEQQgghhB1JEIQQQghhRxIEIYQQQthxKIlGNYu2oXFyKommxT2mHvd7WYcgiknG6j7jNbSsIxDFYTKVdQT/WiWSIAghhBDi1io+pMcpI/dvbWNy15NcQvHcTokkCJYXumDJKO2uiL9Ls/4Ult4hZR2GKAYZq/uLZv0plI09yzoMURxdVpV1BP9acg+CEEIIIexIgiCEEEIIO5IgCCGEEMKOJAhCCCGEsCMJghBCCCHsSIIghBBCCDuSIAghhBDCjiQIQgghhLAjCYIQQggh7EiCIIQQQgg7kiAIIYQQwo4kCEIIIYSwIwmCEEIIIexIgiCEEEIIO5IgCCGEEMKOQ1kHIIQQQoh7Y8aMGRw/fhyVSsWkSZN4+OGHbevi4+MZN24c+fn51K9fn6lTp96xLZlBEEIIIcqBgwcPEhMTww8//MD06dOZNm1aofUzZ87kueeeY9WqVWg0GuLi4u7YniQIQgghRDmwb98+OnXqBEDNmjXJzMwkOzsbAKvVyuHDhwkLCwNgypQpVK5c+Y7tSYIghBBClAPJycl4enralr29vUlKSgIgNTUVvV7PvHnzGDRoEHPmzEFRlDu298Ddg7AjycD4qAQMZitVXLQsblyZQBdtoTpxufk8eySOs9kmKmg1fPJwJdp561gem87ok9fxd77xtr1Xz5fH/SvQZW8M5w0mXDU3cq5TYTVLrV/l1d2MF8C6+EwmRCWSY7HSwtOFxY0qU0GrAeDb2HRGnojn04f9ebqKR6n3rbwpqX3rQJqRsSevk2m2otOoeaduRR7zcyvt7j0Q8s1WJn5zho/WXeTKVx0J9HEhO8fMK19EsvdMGnn5VqY+XYdBoYEAnI7NYsRnJ0lIz8NBo+KdAbXp29q/jHvx4PrrAV9RFFQqle3/CQkJPPHEE7zyyiu8+OKL/Prrr3To0OG27T1QCYLBbOXpw1f56ZGqNPFwYU50MiNPxLO+ZdVC9Z49Eke4n56trauxI8nAp5fSaOetI91spX9ABT5raD8tk5Fv4bumgbTyci2t7pR7dztelwx5jDpxnV1tqhOs0/LaqQR+SshmQKA7s84nsy/VSB29Uxn1rnwpqX1LURSeOnSVzxr685ifG5GZubTffZmLnWvh/v+Jnrh3Hn/vEE1rFk6Wp/1wHkOuhagFHYhLzaXla7tpU8+LoEquPDXrCOMeD+bZTlU4eTmT1m/soWNDH9x12tu8gihJfn5+JCcn25YTExPx8fEBwNPTE39/f6pWLdgnW7Vqxfnz5++YIBTrEsOGDRvo1asXffv25ddff72L8MvWjmQDQTpHmni4APBSdS+2JWaTZbbY6sTm5HMkI4dRQV4AhFXUsaJZQbackW+57YdShtmKh3xg3VN3O17Lr2bQ19+NmnpH1CoVHzWoxIBAdwA6+LiytkUV9A5yle1eKKl9Ky3fyrVcMx19CmaEGlRwxlWj5pIxv6S79EB6u39tpj5dp1DZz8eSGNoxELVaRaCPC71bVmL9getYLApvPlWTwaEBADxUvQJOWjWXEoxlEboA2rRpw9atWwGIiorC19cXvV4PgIODA1WqVOHy5csAnDp1iqCgoDu2V+QMQlpaGgsWLGD16tUYjUbmz59P+/bt77IbZeN8dh41XG9ktnoHNd6ODkQb8mjsXvDBdjwjlyBXRyZGJbApIRs/Zwc+auBHY3cX0vOt7E8z0vr3S2TkW+jmq+e9er44adSk51uYciaRc9l5OGtUvFm7Ij0ryTTo3bjb8TqRmUsVFy1d98ZwJSefUB8ds0P8cHVQ09JTZnrupZLat7wcNTR2d2bFtUyGVvVgd4oRBzXU0zuWVVfLtUfqetqVqVQqLNYby3pnDdHxBjQaFRHtAmzlB86moShQO0BfGqGKW2jSpAkhISFERESgUqmYMmUKa9aswc3Njc6dOzNp0iSmTJmCyWSiVq1athsWb6fIBGHfvn20atUKvV6PXq+3e2zifmK0WHHWFD5jdNGoMJhvXLfJyLdwMjOXt2pXZHaDSnwZk8aTh65yJqwmDSs44e2o4ZVgL3ItCo8fvMIH0Sm8Xaci/SpXoGclN7r66tmTYqTHgSscahdMTfkg+8fudrzS862cyzawrXU1dBo1fQ/G8v75ZKbV8y3trpR7JblvLWzoT/i+GF47dR2jReH7poE4aWTmp7R0auTDpz9dpnMjHxIz8li3/zrtGngXqhOblMPTc44y78UQXJ1kJrUsvfbaa4WW69ata/t/tWrV+Prrr4vdVpF72dWrV1EUhbFjxzJw4ED27dtX/Ej/ZXQOanJvToUp+GC7eZq5glaDn5MDvfwLzv6fr+pBap6Fc4Y8nq7iwfhaPrho1Hg6ahgb7M1PCVkAfPKwP119CzLnNt6utPd2ZXtSdin1rHy62/Fy16rp5e+Gr5MDOgc1L1f3lDEpISW1b+VYrPQ7FMuKZoEkdavLofbBvHw8jhhjXqn270H2dv9aVPZ2puErvzH80xOEN/XF46Z7DM5ezSb0zX1M6FeTpzsElmGk4l4r1k2KCQkJfPLJJ8TFxTFkyBB27txpuzPyflJH78j3VzNsy0kmM2n5VmrpbpzlB7lqyTJbsSoKapUKlUqFWgUaFVw05OHtqLFdK1UArUpFnlXhXLaJBhWcbe0ogFZ9/71H/yZ3O17VXbVk5t84aGlUKjT34e/t/aCk9q1TWSYsCnSsWJB813dzoqbekUPpuVRzldm50qBzdmDxKw1ty8/NPU77BgX38lxLySH8nQN88Ew9nmx752fqxf2nyBkEb29vGjdujIODA1WrVkWn05Gamloasd1zoT46rubkszul4CaaTy6l0t1Pj+6ms5wGFZypoXNkcUw6AKviMvHQaqjh6siMc8lMjEpEURRyLVYWXk7jMT89eVaFDnsu29o9mZnL7hSj7cYq8c/c7XgNCvRgZVwGV3PysSgKS2LT6FhRxqQklNS+Vc1FS3q+hUNpOQBcMeYTlWWSexBK0azV0by6OAqAqCtZ/HI8id4tKwEw4rNIxvYKkuSgnCpyBqFt27ZMmDCBF154gfT0dIxGY6EvYrifuGjUfNcskFdOxmMwW6mhc+SrxgEcTMthyplENreqBsD/mgcy4I+rfBCdTEUnB35oFoiDWsUHIX68dDyOejsuoAbC/fSMq+GNk0bNimaBjD15nRyrFWe1iq+bBBCkkw+xu3G349XQ3ZmZ9f1ov/syWrWKtl6ujK9Z8MhPt30FNy5eycnnfHYeM84n2567F39fSe5bSxsH8OKxOExWBbUKZtb3I+Sm2TpxbySkmegwaa9tOXTSPhw0Kra825Jn5x6nxgs7cHFUs/Q/jfDQa4lLyWXjwQTOXM1m4eYY23YfPFufni38yqIL4h5TKUV9lRKwYsUKfvrpJ3Jychg+fDgdO3a8ZT2TyURkZCT15o7DKSP5lnXEv4dm/SksvUPKOgxRDDJW9xfN+lMoG3uWdRiiGPK6rCIyMpIGDRrg5FSy34tyN8dIk7sPp8d8VCpx/qlY9yBEREQQERFR0rEIIYQQ4l9CnhUSQgghhB1JEIQQQghhRxIEIYQQQth5oP5YkxBCCFHWVC09UJmsRVe8eRun0v+LszKDIIQQQgg7kiAIIYQQwo4kCEIIIYSwIwmCEEIIIexIgiCEEEIIO5IgCCGEEMKOJAhCCCGEsCMJghBCCCHsSIIghBBCCDuSIAghhBDCjiQIQgghhLAjCYIQQggh7EiCIIQQQgg7kiAIIYQQwo4kCEIIIYSw41ASjWoWbUPj5FQSTYt7TLP+VFmHIIpJxur+ouq5saxDEMVhMpV1BP9aMoMghBBCCDslMoNw/ZHWaBITS6JpcQ8FXotlh6pOWYchiiFMOcvVgCplHYYopsBrsSgbe5Z1GKI4uqwq6wj+tWQGQQghhBB2JEEQQgghhB1JEIQQQghhRxIEIYQQQtiRBEEIIYQQdiRBEEIIIYQdSRCEEEIIYUcSBCGEEELYkQRBCCGEEHYkQRBCCCGEHUkQhBBCCGFHEgQhhBBC2JEEQQghhBB2JEEQQgghhB1JEIQQQghhRxIEIYQQQthxKOsAhBBCiAeJKqgCKov1722jqVBC0dyezCAIIYQQwo4kCEIIIYSw88AlCE5tWuO7ZRN+v+3C5/vlaPwrFVrv0v0x/H7dWegn8FosKp0OAE21avhu2YTPiu8KN6zR4PH+DCodOkClg/vRDR1SWl0q1zxDH6H54TW0PLOFRtu+winAz66O/zN9aXnqJ1pGbaLRtq9wqVXdts4luArND6+h0fYlt2zf0d+Xdul/UGlon5LqwgPjbvct5/Bw/Hb/RqU/DuL1xeeo9Hrbtq5P9KXyuTO49pVxKkkbDlyn8ZjfqD9iF4+O30NkTCaKojBh6WnqDt9JveE7mbj0tN12cSm5eERs4etfYssgalFSHqgEQeXigtenC0h77Q0S2nUgd9dveLw/o1CdnJ82kdA+1PaTOXsOxp82oRgMONQIxmfpEvKOH7dr223kCDQ+Plxv2YrEx3rg2rs3Kg+P0upauaR2dSFkxUecHvYWB+qGk7J1N3U+e6dQHdc6wdT48A2Odn6WA/UfI3H1Nup9VTCmrrWDePjHz8k8dPK2r1F77pvkp2WUZDceCHe7b2mqVMFzxnSSBw3hevOWWOLjcO7UCSjYt1x69MB84UJZdO2BcS0lh2c+Ps7yVxsT9WkHBrQP4OUFJ/nh9zh+PZnC8XntODq3Hb8cT2b13vhC245ddApPvbaMIhclpcgEITIyknbt2jF48GAGDx7MtGnTSiOuEuHUtg3mK1fIj4wEwLBsGc7t29vOYOw3cKLCG6+T8V7BB52SayLpqQjyDh+xq6rr35/M+Z+A1Yo1OZmkvk+gpKeXWF8eBJ5hj5BzMZbso1EAxC1cgVfXtmj0N8ZLV78GOecvkxeXCEDajv3oGtQCwJpr4mjYUDL2Hbtl+17h7VDrXEjfdbCEe1L+3e2+5fpEX3I2bcZy+TIoChlT3iVn3ToATHv3kvLscyjZhtLoygNLq1Hz3WuNqV/VDYC29bw4FZvFqj3xDO1YBSetBmdHDc90qsLK3XG27TYfTsRgMtOhgXdZhS5KSJEJgtFopGvXrixbtoxly5bx9ttvl0ZcJcIhOBjL5RjbsmI0Yk1Lw6F69VvW10VEkHfoDywxBdtYrl3DmphoV0/l6opD9Wo4Nm6E77Yt+G7fisvjj5dIHx4krrWrk3PhxpSlxWAkPyUdl5pVbWUZ+4/jUqMqupCCpMD3iS6kbd8LQO6VOPKuJ92ybbWLMzVnv8G5UVNLsAcPjrvdt7T166Hk5+Pz/XL8fv8Vj5kzUDk7A5B39NYJnri3fD2cCG/qa1vefCSRlrU9ORdnoEYlV1t5jUqunLmaDYDRZOH1r6KY/1KDUo9XlLwiH3M0GMpP1q5ycUExmQqVKbm5qFxdb1FZhdtLL5D8zHNFtqt2L3j8RFO5Moldu6GtX4+Kq1eRf/KkTIveBY2rC9bcwuNlzTGh0d0Yr7z4RC5M+ojmx9ZhyczGYszlaPtBRbYdNHkkCd/9SO6lq/c87gfR3e5b6goV0AbXIKl/BIrRiPdXX+L2ymgyP/iwpEMXt/DL8WQ+Xn+JX6Y/Qq/ph3B2vHEu6eKowZBrAWDqinMMaB9AcKXbzBSJ+1qxZhAOHz7MsGHDePrpp9m/f39pxFUiFKMRlZNToTKViwuK0T4JcmzaFKvBiPncuSLbtWZmAWD47ntQFPJPRWHatx+ntm3uTeAPKIvBiNq58HipXZ2x3DTVrG9Uj+pvDmdfcCd+927JhQlzeGjDZ3dsVxdSC6/wR7ny4eISiftBdLf7ljUzi5ytW7GmpKDk5JD9zTKc27Ur8biFvXX7r/Ps3GNsfLs59au6oXPSkJt345l9o8mC3sWByJhMth5J4vU+NcowWlGSipxBqFu3LiNHjqRjx45cunSJZ599lm3btuHo6Fga8d1T5uhoXPvcmPpXe3mhdnfHfPGSXV3nTh3J3bGjWO0qBgOW1FTUbm5Y/iy0WAp+xD9mPHMRv6d72pa1Pp5oPd0xnr8xle3ZsRUZe49iii24aSrxh02EfPshWh9P8pPTbtmuT89QnKtUovWVnQA4uLtRsU9nnAL8iJmxsAR7VH7d7b5liY1F5eZ2U4EVxSr7T2n7+VgSYxedYuu7LalXpWA86gbqOXMtm86NKwJw6koW9avo2XgwkdjkHKo9/wsAGcZ81u6/zrWUXN58qlaZ9UHcO0XOINSoUYOOHTsCEBQUhI+PDwkJCSUeWEnI3bsPjb8/js2bA6B/7llyfv4FJSfHrq62fn3M0dHFbtu4ajX6F18AQFOlCk6tHsG0b9+9CfwBlbbzAM6BlXBv0xSAwNGDSf5xJ1bjjfEynr2Ee+vGOHgVPDHi3b09pvjE2yYHADEzv+B3n0fY49+WPf5tSfxhE+fGvCfJwV24233LuHo1rj17FDwaqVaji+iP6ffdpRK7KGA0WXhu3nFWT2xqSw4Anmzrz1fbYzHkmsnOMbPk51gi2gUw8cmaJC/vSvw3nYn/pjP921bm4xdCJDkoYzNmzKB///5ERERw4sSJW9aZM2cOgwcPLrKtImcQVq1ahdFoZMiQISQlJZGSkoKfn/2z6PeF3FxSh4/AY8Z01C6umC9fJvU/49A2aoT766+R/PSNa9caf38siYVvcNMNHoR+2POo3dxQubnh9+tO8o4dI23Mf8iYOQuv2R9S6eB+FIOB9LcnY75wsbR7WK5Yc01E9v8PtRdMRqNzISf6CqefmYBb84cInjaG4+HDSPlxJ9ebhtBs/w8oVgVLZjaRT44FoPJLEVQZOxQHdz0OFfS0PL2ZzIMnOD10fBn3rBy6y30rP+o0Ge/NoOLaNShmM3kHDpL1yQIAfJZ/iyYwAE1AAO7BwbiNeYWM92eRu2VLqXaxvFu//zpJGXkMmnO0UPmuGa0Jb5JB4zG/oVKpGNCuMj1b3KfHgHLu4MGDxMTE8MMPPxAdHc3EiRNZuXJloTrR0dEcOnQIrbbox1JVyv+1d+fxOZ35/8dfd8ieSLNZKghC0qJjMq0xaCkJHaXTEVWl31GjFNXSX1UttbRFo5u0IaXT0mpqC6klXdLaSu20lkjRhBJkFWSTBffvD5MMbiI0d2657/fzr5xzrnPyOa4H5+26zmI0GstrcO7cOUaPHk1BQQHFxcWMGDGCjh07XrdtUVERCQkJ+Dw7hBrXudtf7ix+J1NYZwi0dBlSAZ2NhzhRv4Gly5AK8juZgnF1z5s3FIsr7rqMUnhxhAAAHaBJREFUhIQEWrZsieM199FUttJrZIv0CBwv3tpj8EU17uJAnVHl1vnBBx9w991388QTTwDQrVs3li9fjtsVLx179tlnGTx4MLNmzeKLL74o93fedATBw8OD//znP7dyHiIiIlLFsrKyaNGiRdmyt7c3mZmZZQEhNjaWNm3aUL9+/Qodz6bepCgiImKtrp0QMBqNGAwGAM6ePUtsbCwDBw6s8PEUEERERKxAnTp1yMrKKlvOyMjAx8cHgG3btpGdnU3//v0ZMWIEBw4cYPr06Tc6FKCAICIiYhXat29PfHw8AImJidSuXbtseuGRRx7hm2++YenSpcyaNYsWLVowfvz4co9303sQRERE5M4XHBxMixYt6Nu3LwaDgcmTJxMbG4u7uzuhoaG3fDwFBBERESsxevToq5aDgoJM2vj5+d30CQbQFIOIiIhchwKCiIiImFBAEBERERMKCCIiImJCAUFERERMKCCIiIiICQUEERERMaGAICIiIiYUEERERMSEAoKIiIiYUEAQEREREwoIIiIiYkIBQUREREwoIIiIiIgJBQQRERExUdMcB627bQuOjo7mOLRUss7GQ5YuQSrI72SKpUuQW2DoudrSJUhFFBVZuoI7llkCAiSY57BSyf4C7LZ0EVIh6qvqRf1VfbSs+l/ZJADsCm5tn0sukG+ecm5EUwwiIiJiQgFBRERETCggiIiIiAkFBBERETGhgCAiIiImFBBERETEhAKCiIiImFBAEBERERMKCCIiImJCAUFERERMKCCIiIiICQUEERERMaGAICIiIiYUEERERMSEAoKIiIiYUEAQEREREwoIIiIiYkIBQUREREwoIIiIiIgJmwsI69btJDi4P4GBvQgNHc6JE+kVbmM0Ghk7NpLAwF4EBYUxbtyssn2OH08jNHQ4zZv3Iji4P+vX76qyc7Jm6q/qQ31lXUpKLjB6dAQGw/1X9eVXX62nWbN/4ufXnbCwV8jJybNglWJONhUQ8vPP07fveD75ZCKHDsXSrdvfGDYsvMJtliz5ng0bdrNv32L27FnImjU7WL58LQBDhkzj0Uc7cPhwLHPmjOOppyZw/nxhlZ+jNVF/VR/qK+vzj3/8P1xcnK5ad/ToSYYPD+fbbz/k+PE4GjSoS1zcTxaqUMytQgGhsLCQLl26EBsba+56zGrdup00aVKf4OAgAIYODSM+fiu5ufkVahMTs4ZnnumBo6MDTk6ODBzYk6VL13DuXB7r1u1k8OB/AtCmTUsaNqzDhg27q/4krYj6q/pQX1mfSZMG88YbQ69aFx39LWFhnQkIaICdnR0RES/Tr98jFqpQzK1CAeGjjz7irrvuMnctZnf48HGaNvUrW3Zzc8Hb24OkpJQKtbl2W9Omfhw8+DtJSSn4+nri6upssk1un/qr+lBfWZ+2bVuZrNu79zAODvZlUz5Dh06noECjOdbqpgEhOTmZpKQkOnXqVAXlmFdBQSFOTg5XrXN2diI/v7BCbS5vc7xivSP5+edvsM/lbXL71F/Vh/rKNpw9m8cPP2znyy+n8ssvX5KcfILp0+dZuiwxk5sGhBkzZjB27NiqqMXsXF2dKCwsvmpdQUEhbm7OFWrj6upMYWHRNetd/rv+evu4mOEsbIf6q/pQX9kGDw9XHn+8E7Vre+Hq6sywYb35/vttli5LzKTcgLBixQpat25NgwYNqqoeswoK8r9qaDIz8wxnzuTQrFnDCrW5dtuBA0e4997GBAT4kZV1lrNnc6/Z1sScp2P11F/Vh/rKNjRuXJ9z5/731EKNGnbUqFHDghWJOZUbEDZs2MDatWvp06cPMTExREVFsWXLlqqqrdI9/PD9nDiRwU8/7QEgMnIJPXp0uGp+s7w2ffqE8Omnq8jPP09eXgHz5q3iqae6UauWG6Ghf2X27KXA5ZuxTp8+R8eOwVV/klZE/VV9qK9sw//9X3eWLl3DiRPpXLx4kXnzVhES0sbSZYmZ1CxvY0RERNnPkZGR1K9fn3bt2pm9KHNxdnZiyZLpPP/8DPLzzxMQ0IDPPpvMjh0JTJw4h/j4WTdsA9C7dwi7dx+kdet+GAwG+vXrRs+eDwEwZ844BgyYwvz5q6lVy5Vly2bg6OhQXjlyE+qv6kN9ZV3S00/TseOQsuVOnZ6jZs0arF37EW+//QIdOjyLvX1NHnywNWPHPmO5QsWsDEaj0ViRhqUBoVevXjdsU1RUREJCAi1bgqPjDZvJHeMvgB4Xqx7UV9WL+qu6KCpq+d/rVksczXzhKr1GtnCNw9Gu4Nb2veTCgfweVVJnqXJHEK70wgsvmLMOERERuYPY1JsURUREpGIUEERERMSEAoKIiIiYUEAQERERExW+SVFERET+OINnMwz2Jbe2T4k95N+8XWXSCIKIiIiY0AiCiIiIlZg+fTp79+7FYDAwfvx47rvvvrJt27Zt4/3338fOzo7GjRszbdo07OxuPE6gEQQRERErsGPHDo4dO8aSJUuYOnUqb7755lXbJ02axIcffsjixYvJz89n06ZN5R5PAUFERMQKbN26lZCQEAACAgLIyckhL+9/H9eKjY2lbt26AHh5eXHmzJlyj6eAICIiYgWysrLw9PQsW/b29iYzM7Ns2c3NDYCMjAy2bNlCx44dyz2eAoKIiIgVuPbTSkajEYPBcNW606dPM3ToUCZNmnRVmLgeBQQRERErUKdOHbKyssqWMzIy8PHxKVvOy8tj8ODBjBw5kg4dOtz0eAoIIiIiVqB9+/bEx8cDkJiYSO3atcumFQDCw8MZMGDATacWSukxRxERESsQHBxMixYt6Nu3LwaDgcmTJxMbG4u7uzsdOnRgxYoVHDt2jGXLlgHQo0cPnnzyyRseTwFBRETESowePfqq5aCgoLKfExISbulYmmIQEREREwoIIiIiYkIBQUREREwoIIiIiIgJBQQRERExYaanGFoCjuY5tFSyv1i6AKkw9VX1ov6qHoosXcAdy0wB4dYepRBL+Quw29JFSIWor6oX9Vf10dLSBdyxNMUgIiIiJhQQRERExIQCgoiIiJhQQBARERETCggiIiJiQgFBRERETCggiIiIiAkFBBERETGhgCAiIiImFBBERETEhAKCiIiImFBAEBERERMKCCIiImJCAUFERERMKCCIiIiICQUEERERMVHT0gWIiIjYlLsCwfEW9ykCTpijmBvTCIKIiIiYUEAQEREREzYXENat20lwcH8CA3sRGjqcEyfSK9zGaDQydmwkgYG9CAoKY9y4WWX7HD+eRmjocJo370VwcH/Wr99VZedkzdRf1Yf6ynotX76W1q37ERQURocOg0hISLJ0SVIFbCog5Oefp2/f8XzyyUQOHYqlW7e/MWxYeIXbLFnyPRs27GbfvsXs2bOQNWt2sHz5WgCGDJnGo4924PDhWObMGcdTT03g/PnCKj9Ha6L+qj7UV9br+PE0hg59i5Ur3+PgweU88UQI//73G5YuS6rATQPC+fPnGTlyJE8//TRPPPEE69evr4q6zGLdup00aVKf4OAgAIYODSM+fiu5ufkVahMTs4ZnnumBo6MDTk6ODBzYk6VL13DuXB7r1u1k8OB/AtCmTUsaNqzDhg27q/4krYj6q/pQX1kve/uaLFw4lUaN6gHQpcsDHDp0zMJVSVW4aUBYv349LVu2JDo6moiICMLDw2+2yx3r8OHjNG3qV7bs5uaCt7cHSUkpFWpz7bamTf04ePB3kpJS8PX1xNXV2WSb3D71V/WhvrJe9er5EBraFoALFy7w2Wdx/OMfHS1clVSFmz7m2L1797KfU1NTqVOnjlkLMqeCgkKcnByuWufs7ER+fmGF2lze5njFekfy88/fYJ/L2+T2qb+qD/WV9fvgg0W88cYnBAT4sWLFe5YuR6pAhd+D0LdvX9LS0pgzZ4456zErV1cnCguLr1pXUFCIm5tzhdq4ujpTWFh0zXqX/66/3j4uZjgL26H+qj7UV9Zv5MinePHFvixeHE+7dv8mMXEpzs5Oli5LzKjCNykuXryYjz76iFdeeQWj0WjOmswmKMj/qqHJzMwznDmTQ7NmDSvU5tptBw4c4d57GxMQ4EdW1lnOns29ZlsTc56O1VN/VR/qK+v1669HWbNmOwAGg4GnnnqEnJx83YdgA24aEBISEkhNTQXgnnvu4eLFi2RnZ5u9MHN4+OH7OXEig59+2gNAZOQSevTocNX8Znlt+vQJ4dNPV5Gff568vALmzVvFU091o1YtN0JD/8rs2UuByzdjnT59jo4dg6v+JK2I+qv6UF9Zr8zMM/zrX5M5dSoTgM2b91BScoEmTepbuDIxt5tOMezatYuTJ08yYcIEsrKyKCgowNPTsypqq3TOzk4sWTKd55+fQX7+eQICGvDZZ5PZsSOBiRPnEB8/64ZtAHr3DmH37oO0bt0Pg8FAv37d6NnzIQDmzBnHgAFTmD9/NbVqubJs2QwcHR3KK0duQv1VfaivrNdDDwUzfvxAQkKGc+nSJRwdHVi8eBq1arlZujQxM4PxJvMFhYWFTJgwgdTUVAoLCxkxYgSdO3e+btuioiISEhJo2RIcb/U902IBfwH0uFj1oL6qXtRf1UVRUcv/Xrda4mjmC9cfuUYWFUFCAlVSZ6mbjiA4OTnx3nu6Y1VERMSW2NSbFEVERKRiFBBERETEhAKCiIiImFBAEBERERMKCCIiImJCAUFERERMKCCIiIiICQUEERERMaGAICIiIiYUEERERMSEAoKIiIiYUEAQEREREwoIIiIiYkIBQUREREwoIIiIiIgJBQQRERExoYAgIiIiJhQQRERErMT06dN58skn6du3L/v27btq25YtW+jduzdPPvkks2fPvumxFBBERESswI4dOzh27BhLlixh6tSpvPnmm1dtnzp1KpGRkSxatIhNmzaRlJRU7vEUEERERKzA1q1bCQkJASAgIICcnBzy8vIASElJwcPDg3r16mFnZ0fHjh3ZunVrucerWZnFGY1GAIqLmwMOlXloMYsioKWli5AKUV9VL+qv6qK4uBj43/WrKpSUmGefrKwsWrRoUbbs7e1NZmYmbm5uZGZm4uXlVbbNx8eHlJSUco9XqQGh5L9ncPjw4co8rIiIiFmVlJTg5ORk1t9Ro0YNatSowaFDF//Q/jdybcgxGo0YDIbrbgPKtt1IpQYEV1dXmjdvjr29/U1/sYiIiKUZjUZKSkpwdXU1+++qWbMmLVu25OLF2w8INWve+LJdp04dsrKyypYzMjLw8fG57rb09HR8fX3Lr/e2qrwBOzs73N3dK/OQIiIiZmXukYMr1axZs9yL/B/Rvn17IiMj6du3L4mJidSuXRs3NzcA/Pz8yMvL48SJE9StW5f169fz7rvvlns8g7EqJ15ERETEbN5991127dqFwWBg8uTJJCYm4u7uTmhoKDt37iwLBV27dmXQoEHlHksBQUREREzoMUcRERExoYAgIiIiJhQQRERExIQCQiXTLR13ltKXoIiI+V26dMnSJUglUkCoJKXBoKioyMKVSKmjR48yadIk0tLSLF2KXCEvL09B2ooYjUZSU1OBy4+6i/VQb1YSg8HA5s2bGTZsGMuXL+fcuXOWLsmmHThwgMaNG+Pi4sKsWbNIT0+3dEkC/PLLLwwfPpyff/6ZCxcuWLocqQT5+flERkby1ltvsWDBAoDbfhGQ3FkUECrJoUOHiIuLIzQ0lG+//ZaVK1eSmZlp6bJsVlRUFP/617+YNGkS7u7uREREKCRYWEpKCufPn+f06dNs3ryZhIQEXUiquUuXLuHm5kaXLl2IiYnh1KlTwOU3/mmUqPpTQPiDjEYj6enpDBkyhKCgIPr168fIkSP5+eef+e6778jIyLB0iTal9B+l2bNn4+XlxeDBg3n11Ve56667FBIsaP/+/YwcORKDwUCvXr1wcnIiLi6O/fv3l33DRfPX1Y+dnR1btmxhy5YtvP3223z99dcsXLgQ+N97/tWv1ZcCwm0qvRAZjUbq1KnDoEGD+PDDD0lJSaFVq1YMGTKEzZs388033+hGuSpy5YdJACIiIvD29mbQoEFlIeHDDz8smy+VquPi4sLFixfZtGkTjRs3ZsiQIbi4uBAXF0dCQgKAwnQ1lJKSwpdffkmrVq0ICQkhKiqKqKgovvnmG3JzcwHdl1Cd6U2Kt6H0QvTTTz/x9ddfY29vz5AhQ9i5cycRERFER0fToEEDEhISsLOz495777V0yTZl6dKlHD9+HH9/f3r37s24cePIyMjg008/ZcqUKRgMBl577bVyv4omlW/GjBnExMTw0ksv0b9/f4qLi5k9ezZweUh6+/btzJ07F1dXV33s7Q52ZRA/cOAAixcvJi8vj5dffhk/Pz/27dvHsGHDuHTpEh9//DGtWrWycMVyuxQQbsGFCxfKPrLx888/ExERwdChQzl16lRZMNizZw/Tpk0jNjaWBg0aWLhi27N48WI2btxIWFgY0dHRtGnThmHDhjFlyhQOHTrEokWLOH36NN7e3pYu1eb8/vvv7N27l0WLFtG/f3969uwJwMCBA8nKyiIiIoKmTZtauEopT2k42L17N9u3b6dFixbk5eWRnp7OuXPn6Nu3L/Xq1ePkyZPk5uYSFBRk6ZLlDzDPJ6WsUHZ2NsuWLeOZZ57BwcGB48eP07p1a9q1awdc/tT1c889x+rVq0lNTeXkyZMKCFWg9B8so9HIpUuXOHbsGMOHD2fPnj3Y2dmVjRwMGTKE8PBw0tLSqFu3rqXLtkn+/v74+/vj4eHBrFmzcHJyom3btly4cIGZM2cqHFQDpU9rzZ49m3bt2rFr1y4KCgpo0qQJtWrVYsGCBQwYMID69etbulSpBDWmTJkyxdJFVAfOzs74+vpSUFBAbm4uxcXF/PbbbzRr1gwnJycCAwNJS0ujfv36dO3aFT8/P5M5calcV/75njhxAg8PDzw8PDh+/Djfffcdc+fOJScnh+XLlxMfH09kZCReXl4Wrlr8/f2pV68e4eHh2NnZ8dJLL+mCUo2sWLGCxx9/nLCwMGrXrk1BQQE5OTkEBgaSmppKs2bN8PT0tHSZUgk0glABFy9epEaNGjRs2JAZM2aQlpbGiBEjyM/PZ+HChXTu3BmAbdu2lQ2bAgoHZlb657ts2TJiYmIICQnhkUcewcHBAX9/f2rWrImjoyOjR4+mefPmZd9FF8t78MEHef311/Hz89PF5A5XGsSLi4txcHDg4sWLxMbG0q5dOwICAsjNzWX58uUMGjSI+++/H1dXV0uXLJVE9yDcxJVzbkeOHOHxxx9nxowZAPTp04evv/6akpISEhMTee655/jb3/5m4Ypty969e5k+fTrvvfceNWvWpG7duqSlpfHqq6/SqFEjNm/ezPz582nYsKGlSxWptn788Ue+//57nJyceOWVVxgzZgze3t5MnjyZpKQkpk6dyvTp07n77rstXapUIgWECvjxxx+JioqiR48edO/eHW9vb8LDw7l48SL9+vWjcePGZGdna/i6Clw7bZORkcEnn3zCiy++iLOzc9nd8Lm5ufj6+uLj46Pha5E/ICEhgXfffZfhw4ezcOFC7O3tGTt2LOPHj8fFxYWUlBRefPFFHnroIUuXKpVMD6heR0ZGBtOmTQMuTy+sXbuWN954g+7du7N3716mTp1KWFgYubm5fPDBBxQXF2uYtApcGQ4SExM5cOAAdnZ2ZGZm8sMPP3D+/HkA4uPjsbOz409/+pPCgcgfkJ6eTnR0NA0bNqRNmzZERETg7OzMhAkTmDt3LmPGjCEiIkLhwErpJsXrcHV1xc/Pj5ycHDw9PdmxYwerVq3iq6++wtfXl7y8PI4dO8aECRMICgrCx8dH9xuY2ZXhYP78+axYsYKtW7eSmZlJu3btWL16NSkpKWzfvp3ExER69+6Nh4eHhasWqX5K/67l5+djMBg4e/Ys+/fvx97enmbNmvHwww+zcuVKGjVqREBAALVq1bJ0yWImuknxGqU3JDZp0oSXX36ZkydPsnjxYpKTk3F3d6d27dqkpqYyYcIE0tLSaNy4saVLtnpXhoNDhw6xf/9+oqKi+OCDDzh27BgjRozg3nvv5ddff+W3337j9ddf1z0HIrep9CVws2fPplOnTjg5OdG9e3f27NlDTk4O7dq1Iy0tDRcXF0uXKmamexCuUHohOnz4MAUFBbRu3ZoJEyaQkpLCggULSE5OZtmyZWzfvp1Ro0ZpWK2KrVixgo0bN5KXl0fDhg05c+YMb731Fj/++COOjo7qD5FKkJyczKJFi2jbti1nzpzhyJEjODg44OfnR0xMDL6+vgwaNIjg4GAuXbqkVylbMfXsFQwGA5s2beKll14iLi6Or776imnTpuHp6ckzzzxD06ZN6dixI1OmTNHFqIodPHiQb7/9lg4dOvDoo4+yY8cOhg8fjoODA+np6ezfv58LFy7oC3Iit6H0783Zs2cZMGAAACEhIXTr1o22bdtSVFREo0aNCAsLo169ehQUFAD6zoK10wjCFUpKSnj//ffp0qUL999//1XbxowZw6lTp4iOjrZQdbYtOzubzz//nKNHj/Lggw9SXFzMqlWr+Otf/8q6deuIjIzUdI/IH7Bz505cXV3Jzs5mypQpvPPOO/z5z38G4OWXX6ZPnz4EBQWxatUqUlNTef755/XOAytn8/cgXDm/bW9vj8FgICYmpiwg7Nixg6+++oq3336bxMRES5Zq07y8vBg4cCArVqzgyJEjhIaGcs8993D06FGioqJ0z4HIH3T69GlGjBjB559/zsSJE3nllVd44YUXaN68Ob///js1atTAw8ODv//97zg4OCgc2ACbHkEoDQdbt24lOTkZT09PgoODmTdvHrVq1eKFF17gyJEjfPzxx7z55pvY29tbumSbl52dzcqVK0lKSuLZZ5/VqIHIH5SSkoKHhwe1atXi+++/Jzw8nKioKDIzMxk3bhydO3dm4MCBNG7cWK+PtzE2PYFU+uGRuXPnUrduXZYtW8bKlSsJCwvj4MGDDB06lFdffZXQ0FCFgzuEl5cXjz32GIGBgbi7u1u6HJFqqfT/hcnJySxcuJD58+eTm5tL165dGTNmDC+++CK+vr688847bN++neLiYgtXLJZg0yMIADNnzqRTp07k5OTwxRdfMHPmTPLz86lbty4HDhzA3d2dhg0bKjnfYUofRxWR27NlyxY++ugj2rdvT3Z2Nh4eHvTr1w9PT09ee+01fvrpJzZs2MC8efOIi4tj0aJFODo6WrpsqUI2FxBKL/QHDx4kOTmZkydPkpycTE5ODhMmTMDPz4+5c+cyYMAAnJycLF2uiEilK30j7LRp0/Dx8WHv3r2cOnWKgoICWrVqxa5duwgNDSUwMBCAnJwcvRDJBtncFIPBYGDnzp188cUXBAUF0bVrV/bv38+DDz6In58fv/zyC/Hx8WRlZVm6VBERsyguLiY4OJjExERiY2OJjo5m9+7drF69mokTJ3LfffcRGBjIpUuXADSdZ6NsZgShdOQgMzOTOXPmsHHjRqZPn84DDzzA1q1b+fjjj6lfvz4HDx5k1KhRdOjQwdIli4iYxenTp1myZAmbN29m+PDhNGrUiH379uHl5UVgYKC+LSOADQUEgE2bNjFz5kzq16/PDz/8QKNGjZg7dy7+/v6kpqZiZ2dHXl4eTZs2tXSpIiJVZtu2bURGRjJq1CgeeOABS5cjdwibCQjJycm8//77jBkzhkaNGjFx4kRiYmJo3bo14eHh+Pv7W7pEEZEqlZubS1xcHHFxcTz33HN6Q6xcxSYCQnFxMQsWLCAmJoapU6fywAMPUFxczKhRo9i0aRP+/v4sXboUZ2dnS5cqIlKlSkpKyM3NxcvLy9KlyB3GJgICXH7HeHR0NGfPnqV79+4EBwezZs0a0tPTadeunV64IyIicgWbCQhw+S18sbGxbNy4kc6dO7N27VoGDx6sYTUREZFr2FRAADh37hyff/45SUlJhISE8Nhjj+klSCIiItewufcgeHh48PTTTxMcHMyuXbv49ddfFQ5ERESuYXMBAS6/z79nz540a9YMX19fS5cjIiJyx7G5KYYr6X3+IiIi12fTAUFERESuzyanGERERKR8CggiIiJiQgFBRERETCggiIiIiAkFBBERETHx/wHD6bYwejrZ9wAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(blend_soft, plot = 'error')","metadata":{},"execution_count":45,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(lgbm_1, plot = 'error')","metadata":{},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot_model(lgbm_1, plot = 'dimension')","metadata":{},"execution_count":47,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# **See, how we can play and tune our models within just few lines of code (given the knowlegde of models, math and paramters working) this will help automate most of the hectic work of data scientists.**","metadata":{}},{"cell_type":"markdown","source":"**Thanks for checking this notebook, you can contact me here for queries or at [LinkedIn](https://www.linkedin.com/in/muhammad-saad-31740060/)**","metadata":{}}]}