Beyond L1: Faster and Better Sparse Models with skglm

04/16/2022
by   Quentin Bertrand, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset