A machine learning library for regression.
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 incorporates prior knowledge into boosting by supplanting the standard statistical initialization with predictions from a user-specified model. The implementation:
- Enables interoperability between user-specified models and nonparametric statistical methods or supervised machine learning algorithms, i.e., it is not limited to boosting decision trees.
- Is especially suited for 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, follow 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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