Metadata-Version: 2.1
Name: xgbimputer
Version: 0.1.0
Summary: Extreme Gradient Boosting imputer for Machine Learning.
Home-page: https://github.com/leonardodepaula/xgbimputer
Author: Leonardo de Paula Liebscher
Author-email: <leonardopx@gmail.com>
License: UNKNOWN
Keywords: python,machine learning,missing values,imputation
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Development Status :: 4 - Beta
Description-Content-Type: text/markdown
License-File: LICENSE.md

# XGBImputer

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XGBImputer is an effort to implement the concepts of the MissForest algorithm proposed by Daniel J. Stekhoven and Peter Bühlmann[1] in 2012, but leveraging the robustness and predictive power of the XGBoost[2] algorithm released in 2014.

The package also aims to simplify the process of imputing categorical values in a scikit-learn[3] compatible way.

</div>

## Installation

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```console
$ pip install xgbimputer
```

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## License

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This project is licensed under the terms of the [Apache-2](https://github.com/leonardodepaula/xgbimputer/blob/master/LICENSE) license.

</div>

## References

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* [1] [Daniel J. Stekhoven and Peter Bühlmann. "MissForest—non-parametric missing value imputation for mixed-type data."](https://academic.oup.com/bioinformatics/article/28/1/112/219101)

* [2] [XGBoost](https://xgboost.ai/)

* [3] [scikit-learn](https://scikit-learn.org/)

</div>


