Scikit-physlearn amalgamates Scikit-learn, LightGBM, XGBoost, CatBoost, and Mlxtend regressors into a flexible framework that:
- Follows the Scikit-learn API.
- Processes pandas data representations.
- Solves single-target and multi-target regression tasks.
- Interprets regressors with SHAP.
Additionally, the library contains the official implementation of base boosting, which is a reformulation of gradient boosting that
- Regards predictions from any regression model as an inductive bias.
In contrast, gradient boosting regards the prediction from a constant model as an inductive bias.
- Consequently, base boosting generalizes Tukey’s methods of twicing, thricing, and reroughing, as gradient boosting works with a variety of fitting criterion.
The machine learning library was started by Alex Wozniakowski during his graduate studies at Nanyang Technological University.
Scikit-physlearn can be installed from PyPI:
pip install scikit-physlearn
To build from source, follow the installation guide.
If you use this library, please consider adding the corresponding citation:
@article{wozniakowski_2021_boosting,
title={A new formulation of gradient boosting},
author={Wozniakowski, Alex and Thompson, Jayne and Gu, Mile and Binder, Felix C.},
journal={Machine Learning: Science and Technology},
volume={2},
number={4},
year={2021}
}