XGBoost Classifier.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, mean_squared_error\nfrom sklearn.datasets import load_breast_cancer\nfrom xgboost import XGBClassifier\nfrom sklearn.model_selection import cross_val_score","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-01-02T10:41:29.695772Z","iopub.execute_input":"2025-01-02T10:41:29.696217Z","iopub.status.idle":"2025-01-02T10:41:31.210150Z","shell.execute_reply.started":"2025-01-02T10:41:29.696171Z","shell.execute_reply":"2025-01-02T10:41:31.208973Z"}},"outputs":[],"execution_count":1},{"cell_type":"code","source":"# Classification Task: Breast Cancer Dataset\n# Load dataset\ndata_class = load_breast_cancer()\nX_class = data_class.data\ny_class = data_class.target","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-02T10:41:32.157024Z","iopub.execute_input":"2025-01-02T10:41:32.157689Z","iopub.status.idle":"2025-01-02T10:41:32.178459Z","shell.execute_reply.started":"2025-01-02T10:41:32.157643Z","shell.execute_reply":"2025-01-02T10:41:32.177230Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"data_class['data']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-02T10:41:33.185124Z","iopub.execute_input":"2025-01-02T10:41:33.185482Z","iopub.status.idle":"2025-01-02T10:41:33.193579Z","shell.execute_reply.started":"2025-01-02T10:41:33.185449Z","shell.execute_reply":"2025-01-02T10:41:33.192230Z"}},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n        1.189e-01],\n       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n        8.902e-02],\n       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n        8.758e-02],\n       ...,\n       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n        7.820e-02],\n       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n        1.240e-01],\n       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n        7.039e-02]])"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"# Split the dataset into train and test sets\nX_train_class, X_test_class, y_train_class, y_test_class = train_test_split(\n    X_class, y_class, test_size=0.2, random_state=42\n)\n\n# Initialize the XGBoost classifier\nxgb_class = XGBClassifier(\n    n_estimators=100,\n    learning_rate=0.1,\n    max_depth=3,\n    use_label_encoder=False,\n    eval_metric=\"logloss\",\n    random_state=42\n)\n\n# Fit the model\nxgb_class.fit(X_train_class, y_train_class)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-02T10:41:34.094020Z","iopub.execute_input":"2025-01-02T10:41:34.094475Z","iopub.status.idle":"2025-01-02T10:41:34.608565Z","shell.execute_reply.started":"2025-01-02T10:41:34.094429Z","shell.execute_reply":"2025-01-02T10:41:34.607733Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric='logloss',\n              feature_types=None, gamma=None, grow_policy=None,\n              importance_type=None, interaction_constraints=None,\n              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n              max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n              max_leaves=None, min_child_weight=None, missing=nan,\n              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n              n_jobs=None, num_parallel_tree=None, random_state=42, ...)","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 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0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: 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be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n              feature_types=None, gamma=None, grow_policy=None,\n              importance_type=None, interaction_constraints=None,\n              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n              max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n              max_leaves=None, min_child_weight=None, missing=nan,\n              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n              n_jobs=None, num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n              feature_types=None, gamma=None, grow_policy=None,\n              importance_type=None, interaction_constraints=None,\n              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n              max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n              max_leaves=None, min_child_weight=None, missing=nan,\n              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n              n_jobs=None, num_parallel_tree=None, random_state=42, ...)</pre></div></div></div></div></div>"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"# Make predictions\ny_pred_class = xgb_class.predict(X_test_class)\n\n# Evaluate the model\naccuracy = accuracy_score(y_test_class, y_pred_class)\nprint(f\"Classification Accuracy: {accuracy:.2f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-02T10:41:36.474238Z","iopub.execute_input":"2025-01-02T10:41:36.474569Z","iopub.status.idle":"2025-01-02T10:41:36.508205Z","shell.execute_reply.started":"2025-01-02T10:41:36.474541Z","shell.execute_reply":"2025-01-02T10:41:36.503467Z"}},"outputs":[{"name":"stdout","text":"Classification Accuracy: 0.96\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"# Perform 5-fold cross-validation\ncv_scores = cross_val_score(xgb_class, X_class, y_class, cv=5, scoring='accuracy')\nprint(f\"Cross-Validation Accuracy: {cv_scores.mean():.2f} ± {cv_scores.std():.2f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-02T10:41:37.481399Z","iopub.execute_input":"2025-01-02T10:41:37.481926Z","iopub.status.idle":"2025-01-02T10:41:39.406495Z","shell.execute_reply.started":"2025-01-02T10:41:37.481879Z","shell.execute_reply":"2025-01-02T10:41:39.403527Z"}},"outputs":[{"name":"stdout","text":"Cross-Validation Accuracy: 0.97 ± 0.02\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}