skglm: Improving scikit-learn for Regularized Generalized Linear Models

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. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

Authors

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

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