What is scikit-physlearn?

Scikit-physlearn is a machine learning library that naturally handles single-target and multi-target regression tasks. Correspondingly, the Python library amalgamates scikit-learn, LightGBM, XGBoost, CatBoost, and Mlxtend regressors into a unified object that follows the scikit-learn API. Furthermore, the library contains the official implementation of base boosting.

The primary object of interest is the Regressor:

Regressor(regressor_choice='lgbmregressor')

which uses a case-insensitive string, such as 'lgbmregressor', to access a regressor, such as LGBMRegressor. The main methods include fit, predict, and score, as well as:

• Joblib methods for model persistence.

• A search method that bundles GridSearchCV, RandomizedSearchCV, and Bayesian optimization.

• Cross-validation methods, such as cross_validate, cross_val_score, and nested_cross_validate.

• A base boosting method with built-in model selection.

Citation

If you use this library, please consider adding the corresponding citation:

@article{wozniakowski_2021_boosting,
  title={A new formulation of gradient boosting},
  author={Wozniakowski, Alex and Thompson, Jayne and Gu, Mile and Binder, Felix C.},
  journal={Machine Learning: Science and Technology},
  volume={2},
  number={4},
  year={2021}
}