LiblineaR: Linear Predictive Models Based on the LIBLINEAR C/C++ Library
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.
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: |
LKT |
| Reverse imports: |
ILoReg, kebabs, PrInCE, quanteda.textmodels, scBio, SIAMCAT, sweater |
| Reverse suggests: |
flowml, mlr, parsnip, RSSL, tidyAML, vetiver |
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