LiblineaR: A Wrapper for the LIBLINEAR C/C++ Machine Learning Library

LiblineaR is a software wrapper for the LIBLINEAR C/C++ library, developed by Thibault Helleputte. The underlying library is designed for solving large-scale regularized linear classification and regression problems. It supports L1- and L2-regularized classification and regression, multi-class classification methods, cross-validation, and probability estimates.

Use Cases

• Training L2-regularized logistic regression models based on the library's support for probability estimates.
• Implementing multi-class classification using the one-vs-the-rest or Crammer & Singer method mentioned in the description.
• Performing model selection via cross-validation for linear support vector machines as described.
• Solving large-scale linear support vector regression problems with L1- or L2-loss functions.

Strengths

• The description states the underlying LIBLINEAR library is 'particularly fast as compared to other libraries'.
• Supports multiple model types including L1- and L2-regularized classification and regression.
• Includes features for model selection (cross-validation) and handling unbalanced data.

Limitations

• Row count, column definitions, and sample data are unknown, limiting suitability assessment.
• File formats and license information are unspecified.
• The last update date is unknown; freshness is unverified.

Provenance

Source

Thibault Helleputte

Collection Method

A software wrapper for the LIBLINEAR C/C++ library.

Time Range

Temporal coverage is unknown.

Freshness

Last updated date is unknown.

Geography

Spatial coverage is unknown.