skglm: Improving scikit-learn for Regularized Generalized Linear Models

Mathurin Massias, Badr MOUFAD, Quentin Bertrand

Keywords: scikit-learn, optimization, generalized linear models

TL;DR: skglm is a package that improves upon scikit-learn for generalized linear models

Abstract: We introduce skglm, an open-source Python package for regularized Generalized Linear Models (GLMs). It solves many limitations of scikit-learn, that have impaired the use of GLMs by practitioners. Thanks to its composable nature, skglm 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, and unlocking new applications. 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.

Submission Number: 49