skglm

— A fast and modular scikit-learn replacement for regularized GLMs —

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skglm is a Python package that offers fast estimators for regularized Generalized Linear Models (GLMs) that are 100% compatible with scikit-learn. It is highly flexible and supports a wide range of GLMs. You get to choose from skglm’s already-made estimators or customize your own by combining the available datafits and penalties.

Get a hands-on glimpse on skglm through the Getting started page.

Why skglm?

skglm is specifically conceived to solve regularized GLMs. It supports many missing models in scikit-learn and ensures high performance.

There are several reasons to opt for skglm among which:

Speed	Fast solvers able to tackle large datasets, either dense or sparse, with millions of features up to 100 times faster than scikit-learn
Modularity	User-friendly API that enables composing custom estimators with any combination of its existing datafits and penalties
Extensibility	Flexible design that makes it simple and easy to implement new datafits and penalties, a matter of few lines of code
Compatibility	Estimators fully compatible with the scikit-learn API and drop-in replacements of its GLM estimators

Installing skglm

skglm is available on PyPi. Get the latest version of the package by running

$ pip install -U skglm

It is also available on conda-forge and can be installed using, for instance:

$ conda install -c conda-forge skglm

With skglm being installed, Get the first steps with the package via the Getting started section. Other advanced topics and uses-cases are covered in Tutorials.

Cite

skglm is the result of perseverant research. It is licensed under BSD 3-Clause. You are free to use it and if you do so, please cite

@inproceedings{skglm,
    title     = {Beyond L1: Faster and better sparse models with skglm},
    author    = {Q. Bertrand and Q. Klopfenstein and P.-A. Bannier
                 and G. Gidel and M. Massias},
    booktitle = {NeurIPS},
    year      = {2022},
}