scikit-learn-contrib/lightning

Health v2 · maintenance only

Flags: archived no_license

Adoption not part of the score

1782 stars · 217 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library for large-scale linear classification, regression, and ranking that follows scikit-learn API conventions. It supports dense and sparse data with computationally demanding parts implemented in Cython, offering solvers like CD, SDCA, SGD, AdaGrad, SAG, SAGA, SVRG, and FISTA.

Use cases

• train linear classifiers on large sparse datasets
• fit linear regression models with L1 or group lasso penalties
• learn multiclass classifiers on text data like News20
• train online ranking models with PRank
• use SGD, SAGA, or SVRG solvers with a scikit-learn-compatible API
• perform feature selection via sparse linear models

When to choose

• you need fast linear models on large-scale or high-dimensional sparse data
• you want scikit-learn API compatibility with more solver variety
• you need specialized penalties like l1/l2 group lasso or trace norm
• you want variance-reduced stochastic solvers like SAGA or SVRG

When to avoid

• you need nonlinear models like gradient boosting or deep learning
• you need actively developed software with recent releases
• you need GPU acceleration
• you need a license file for compliance-sensitive use

Facets

library · maturity maintenance

machine-learning machine-learning data-science python cross-platform linear-classification linear-regression ranking scikit-learn-api cython large-scale sparse-data optimization

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markdown · JSON · MCP: product_card(name="scikit-learn-contrib/lightning")