Metadata-Version: 2.1
Name: svreg
Version: 0.1.0.post1
Summary: Module to perform regression using Shapley Values.
Keywords: regression,shapley value,game theory,explainability
Author-email: Yannick Stoll <yannick@pixelforest.io>
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: alive_progress
Requires-Dist: black
Requires-Dist: cython>=0.28.5
Requires-Dist: dpcpp-cpp-rt
Requires-Dist: icecream
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pre-commit
Requires-Dist: pytest
Requires-Dist: pytest-cov
Requires-Dist: scikit-learn==1.0.1
Requires-Dist: scikit-learn-intelex>=2021.6.0
Requires-Dist: scipy
Project-URL: Source, https://bitbucket.org/pixelforest/sv_regression/src/master/
Project-URL: original_article, https://www.researchgate.net/publication/229728883_Analysis_of_Regression_in_Game_Theory_Approach

# svreg : regression based on Shapley Values.

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## Installation

For now, svreg is only available on Test Pypi.
To install the package, please run the following command:

```bash
pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple sv_regression
```

## Requirements

See requirements.txt

## Example

Please have a look at the notebook basic_example.ipynb for indications on how to use this package.


## Changelog

- 0.1.0: Initial release

