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model: scikit: Simple Linear Regression
Signed-off-by: John Andersen <john.s.andersen@intel.com>
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‎.travis.yml

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- PLUGIN=.
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- PLUGIN=model/tensorflow
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- PLUGIN=model/scratch
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- PLUGIN=model/scikit
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- PLUGIN=feature/git
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- PLUGIN=feature/auth
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- CHANGELOG=1

‎CHANGELOG.md

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`argparse.ArgumentParser` via the `CLI_FORMATTER_CLASS` property.
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- Skeleton for service creation was added
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- Simple Linear Regression model from scratch
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- Scikit Linear Regression model
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- Community link in CONTRIBUTING.md.
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- Explained three main parts of DFFML on docs homepage
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- Documentation on how to use ML models on docs Models plugin page.

‎docs/plugins/dffml_model.rst

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- default: /home/user/.cache/dffml/scratch
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- Directory where state should be saved
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- predict: String
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- Label or the value to be predicted
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dffml_model_scikit
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------------------
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.. code-block:: console
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pip install dffml-model-scikit
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scikitlr
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~~~~~~~~
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*Core*
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Linear Regression Model implemented using scikit. Models are saved under the
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``directory`` in subdirectories named after the hash of their feature names.
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.. code-block:: console
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$ cat > train.csv << EOF
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Years,Expertise,Trust,Salary
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0,1,0.2,10
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1,3,0.4,20
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2,5,0.6,30
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3,7,0.8,40
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EOF
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$ cat > test.csv << EOF
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Years,Expertise,Trust,Salary
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4,9,1.0,50
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5,11,1.2,60
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EOF
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$ dffml train \
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-model scikitlr \
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename train.csv \
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-source-readonly \
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-log debug
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$ dffml accuracy \
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-model scikitlr \
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename test.csv \
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-source-readonly \
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-log debug
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1.0
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$ echo -e 'Years,Expertise,Trust\n6,13,1.4\n' | \
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dffml predict all \
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-model scikitlr \
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename /dev/stdin \
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-source-readonly \
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-log debug
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[
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{
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"extra": {},
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"features": {
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"Expertise": 13,
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"Trust": 1.4,
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"Years": 6
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},
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"last_updated": "2019-07-31T08:40:59Z",
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"prediction": {
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"confidence": 1.0,
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"value": 70.0
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},
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"src_url": "0"
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}
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]
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**Args**
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- directory: String
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- default: /home/user/.cache/dffml/scikit
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- Directory where state should be saved
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- predict: String
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- Label or the value to be predicted

‎model/scikit/.coveragerc

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[run]
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source =
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dffml_model_scikit
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tests
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branch = True
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[report]
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exclude_lines =
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no cov
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no qa
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noqa
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pragma: no cover
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if __name__ == .__main__.:

‎model/scikit/.gitignore

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*.log
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*.pyc
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.cache/
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.coverage
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.idea/
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.vscode/
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*.egg-info/
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build/
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dist/
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docs/build/
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venv/
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wheelhouse/
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*.egss
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.mypy_cache/
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*.swp
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.venv/
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.eggs/
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*.modeldir
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*.db
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htmlcov/

‎model/scikit/LICENSE

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Copyright (c) 2019 Intel
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MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎model/scikit/MANIFEST.in

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include README.md
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include LICENSE

‎model/scikit/README.md

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# DFFML Models For scikit / sklearn
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## About
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Models created using scikit.
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## Install
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```console
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python3.7 -m pip install --user dffml-model-scikit
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```
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## Usage
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1. Linear Regression Model
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For implementing linear regression to a dataset, let us take a simple example:
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| Years of Experience | Expertise | Trust Factor | Salary |
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| -------------------- | ---------- | ------------ | ------ |
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| 0 | 01 | 0.2 | 10 |
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| 1 | 03 | 0.4 | 20 |
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| 2 | 05 | 0.6 | 30 |
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| 3 | 07 | 0.8 | 40 |
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| 4 | 09 | 1.0 | 50 |
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| 5 | 11 | 1.2 | 60 |
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```console
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$ cat > train.csv << EOF
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Years,Expertise,Trust,Salary
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0,1,0.2,10
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1,3,0.4,20
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2,5,0.6,30
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3,7,0.8,40
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EOF
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$ cat > test.csv << EOF
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Years,Expertise,Trust,Salary
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4,9,1.0,50
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5,11,1.2,60
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EOF
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$ dffml train \
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-model scikitlr \
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename train.csv \
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-source-readonly \
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-log debug
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$ dffml accuracy \
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-model scikitlr \
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename test.csv \
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-source-readonly \
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-log debug
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$ echo -e 'Years,Expertise,Trust\n6,13,1.4\n' | \
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dffml predict all \
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-model scikitlr \
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-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1 \
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-model-predict Salary \
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-sources f=csv \
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-source-filename /dev/stdin \
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-source-readonly \
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-log debug
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```
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## License
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Scikit Models are distributed under the terms of the
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[MIT License](LICENSE).

‎model/scikit/dffml_model_scikit/__init__.py

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