glum is a fast, modern, Python-first GLM estimation library. Generalized linear modeling (GLM) is a core statistical tool that includes many common methods like least-squares regression, Poisson regression and logistic regression as special cases. In addition to fitting basic GLMs, glum supports a wide range of features. These include:
- Built-in cross validation for optimal regularization, efficiently exploiting a “regularization path”
- L1 and elastic net regularization, which produce sparse and easily interpretable solutions
- L2 regularization, including variable matrix-valued (Tikhonov) penalties, which are useful in modeling correlated effects
- Normal, Poisson, logistic, gamma, and Tweedie distributions, plus varied and customizable link functions
- Dispersion and standard errors
- Box and linear inequality constraints, sample weights, offsets.
- A scikit-learn-like API to fit smoothly into existing workflows.
glum was also built with performance in mind. The following figure shows the runtime of a realistic example using an insurance dataset. For more details and other benchmarks, see the Benchmarks<benchmarks> section.
We suggest visiting the Installation<install> and Getting Started<getting_started/getting_started> sections first.
Installation <install.rst> Getting Started <getting_started/getting_started.ipynb> Motivation <motivation.rst> Benchmarks vs glmnet/H2O <benchmarks.rst>
Tutorials <tutorials/tutorials.rst>
Contributing/Development <contributing.rst> Algorithmic details <background.ipynb> API Reference <glm> GitHub <https://github.com/Quantco/glum> Changelog <changelog>
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