skglm: Improving scikit-learn for Regularized Generalized Linear Models

Badr Moufad; Pierre-Antoine Bannier; Quentin Bertrand; Quentin Klopfenstein; Mathurin Massias

We introduce skglm, an open-source Python package for regularized Generalized Linear Models. Thanks to its composable nature, it supports combining datafits, penalties, and solvers to fit a wide range of models, many of them not included in scikit-learn (e.g. Group Lasso and variants). It uses state-of-the-art algorithms to solve problems involving high-dimensional datasets, providing large speed-ups compared to existing implementations. It is fully compliant with the scikit-learn API and acts as a drop-in replacement for its estimators. Finally, it abides by the standards of open source development and is integrated in the scikit-learn-contrib GitHub organization.

skglm: Improving scikit-learn for Regularized Generalized Linear Models

Abstract