Skglm: Improving Scikit-Learn for Regularized Generalized Linear Models
Abstract
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.
Cite
Text
Moufad et al. "Skglm: Improving Scikit-Learn for Regularized Generalized Linear Models." Machine Learning Open Source Software, 2025.Markdown
[Moufad et al. "Skglm: Improving Scikit-Learn for Regularized Generalized Linear Models." Machine Learning Open Source Software, 2025.](https://mlanthology.org/mloss/2025/moufad2025jmlr-skglm/)BibTeX
@article{moufad2025jmlr-skglm,
title = {{Skglm: Improving Scikit-Learn for Regularized Generalized Linear Models}},
author = {Moufad, Badr and Bannier, Pierre-Antoine and Bertrand, Quentin and Klopfenstein, Quentin and Massias, Mathurin},
journal = {Machine Learning Open Source Software},
year = {2025},
pages = {1-6},
volume = {26},
url = {https://mlanthology.org/mloss/2025/moufad2025jmlr-skglm/}
}