Beyond L1: Faster and Better Sparse Models with skglm
Quentin Bertrand ⋅ Quentin Klopfenstein ⋅ Pierre-Antoine Bannier ⋅ Gauthier Gidel ⋅ Mathurin Massias
2022 Poster
Abstract
We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent, working sets and Anderson acceleration. It handles previously unaddressed models, and is extensively shown to improve state-of-art algorithms. We provide a flexible, scikit-learn compatible package, which easily handles customized datafits and penalties.
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