A Python package for single-target and multi-target regression tasks.
Project description
Scikit-physlearn
Scikit-physlearn is a machine learning library designed to amalgamate 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 an algorithmic paradigm for building additive expansions based upon the output of any base-level regressor. The implementation:
Supplants the statistical initialization in gradient boosting with the output of any base-level regressor.
Boosts arbitrary basis functions, i.e., it is not limited to boosting decision trees.
Efficiently learns in the low data regime.
The library was started by Alex Wozniakowski during his graduate studies at Nanyang Technological University.
Installation
Scikit-physlearn can be installed from PyPI:
pip install scikit-physlearn
To build from source, see the installation guide.
Citation
If you use this library, please consider adding the corresponding citation:
@article{wozniakowski_2020_boosting,
title={Boosting on the shoulders of giants in quantum device calibration},
author={Wozniakowski, Alex and Thompson, Jayne and Gu, Mile and Binder, Felix C.},
journal={arXiv preprint arXiv:2005.06194},
year={2020}
}
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