LiblineaR

A wrapper around the LIBLINEAR C/C++ library for machine learning (available at https://www.csie.ntu.edu.tw/~cjlin/liblinear/). 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.

Last 30 days

This package has been downloaded 3,938 times in the last 30 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 68 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 54,350 times in the last 365 days. This work is reaching a lot of screens. A significant achievement indeed! The day with the most downloads was Nov 06, 2024 with 372 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.