Metadata-Version: 2.1
Name: scikit-physlearn
Version: 0.1.6
Summary: A machine learning library for regression.
Home-page: https://github.com/a-wozniakowski/scikit-physlearn
Maintainer: Alex Wozniakowski
Maintainer-email: wozn0001@e.ntu.edu.sg
License: MIT
Download-URL: https://github.com/a-wozniakowski/scikit-physlearn
Project-URL: Paper, https://arxiv.org/abs/2005.06194
Description: # Scikit-physlearn
        
        [![SOTA](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/boosting-on-the-shoulders-of-giants-in/multi-target-regression-on-google-5-qubit)](https://paperswithcode.com/sota/multi-target-regression-on-google-5-qubit?p=boosting-on-the-shoulders-of-giants-in)
        [![Documentation Status](https://readthedocs.org/projects/scikit-physlearn/badge/?version=latest)](https://scikit-physlearn.readthedocs.io/en/latest/?badge=latest)
        [![PyPI](https://badge.fury.io/py/scikit-physlearn.svg)](https://badge.fury.io/py/scikit-physlearn)
        
        [Documentation](https://scikit-physlearn.readthedocs.org) |
        [Base boosting](https://arxiv.org/abs/2005.06194)
        
        **Scikit-physlearn** is a machine learning library designed to amalgamate 
        [Scikit-learn](https://scikit-learn.org/),
        [LightGBM](https://lightgbm.readthedocs.org),
        [XGBoost](https://xgboost.readthedocs.org),
        [CatBoost](https://catboost.ai/),
        and [Mlxtend](http://rasbt.github.io/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](https://arxiv.org/abs/2005.06194>), 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](https://pypi.org/project/scikit-physlearn/):
        ```
        pip install scikit-physlearn
        ```
        
        To build from source, follow the [installation guide](https://scikit-physlearn.readthedocs.io/en/latest/install.html).
        
        ## 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}
        }
        
        ```
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
