Scikit-physlearn

Documentation | Base boosting

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}
}