LiblineaR: Linear Predictive Models Based on the 'LIBLINEAR' C/C++ Library
Version 2.10-8

A wrapper around the 'LIBLINEAR' C/C++ library for machine learning (available at ). 'LIBLINEAR' is a simple library for solving large-scale regularized linear classification and regression. It currently supports L2-regularized classification (such as logistic regression, L2-loss linear SVM and L1-loss linear SVM) as well as L1-regularized classification (such as L2-loss linear SVM and logistic regression) and L2-regularized support vector regression (with L1- or L2-loss). The main features of LiblineaR include multi-class classification (one-vs-the rest, and Crammer & Singer method), cross validation for model selection, probability estimates (logistic regression only) or weights for unbalanced data. The estimation of the models is particularly fast as compared to other libraries.

Getting started

Package details

AuthorThibault Helleputte <>; Pierre Gramme <>; Jerome Paul <>
Date of publication2017-02-13 12:58:40
MaintainerThibault Helleputte <>
Package repositoryView on CRAN
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LiblineaR documentation built on May 29, 2017, 8:45 p.m.