cjlin1/liblinear

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1051 stars · 342 forks observed · 2026-08-28

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

LIBLINEAR is a C++ library and CLI toolset for training large-scale regularized linear classifiers, regressors, and one-class SVMs. It ships train/predict command-line programs plus Python and MATLAB/Octave interfaces, using the same sparse data format as LIBSVM.

Use cases

• train a linear SVM on millions of text documents
• classify large sparse high-dimensional feature data
• fit logistic regression with L1 or L2 regularization
• run support vector regression on big datasets
• detect outliers with one-class SVM
• cross-validate and auto-select C parameters for linear models

When to choose

• your dataset is large and linear models suffice, e.g. document classification
• you need fast training on millions of instances and features
• you want LIBSVM-compatible sparse data format with a simpler linear solver
• you need probability outputs via logistic regression

When to avoid

• your data is small or you are a beginner - use LIBSVM first
• you need nonlinear kernel mappings for accuracy
• your data is unscaled or C is large, which can make the default solver slow

Facets

library · maturity stable

machine-learning machine-learning data-science windows python cpp cross-platform cli svm logistic-regression linear-classification large-scale-learning libsvm-format matlab-interface natural-language-processing linux macos

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