Scikit-physlearn amalgamates 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 is a reformulation of gradient boosting that
Regards predictions from any regression model as an inductive bias.
In contrast, gradient boosting regards the prediction from a constant model as an inductive bias.
Consequently, base boosting generalizes Tukey’s methods of twicing, thricing, and reroughing, as gradient boosting works with a variety of fitting criterion.
The machine learning library was started by Alex Wozniakowski during his graduate studies at Nanyang Technological University.