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}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for scikit_physlearn-0.1.7-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6734b0aff70b10a798c30fa6e96b3c8bd0796bfde11e11961b4c2624ed5f9f05 |
|
MD5 | 0013cff8a6709e9638f43c2b40500c93 |
|
BLAKE2b-256 | fa8bcf6a7a4c993696235917b11b6e8f574719a1e4d0da488576e07ad9632ec6 |