mlearning: Machine Learning Algorithms with Unified Interface and Confusion Matrices

A unified interface is provided to various machine learning algorithms like LDA, QDA, k-nearest neighbour, LVQ, random forest, SVM, ... It allows to train, test, and apply cross-validation using similar functions and function arguments with a minimalist and clean, formula-based interface. Missing data are threated the same way as base and stats R functions for all algorithms, both in training and testing. Confusion matrices are also provided with a rich set of metrics calculated and a few specific plots.

Version: 1.1.1
Depends: R (≥ 3.0.4)
Imports: stats, grDevices, class, nnet, MASS, e1071, randomForest, ipred
Suggests: mlbench, datasets, RColorBrewer
Published: 2022-04-26
Author: Philippe Grosjean [aut, cre], Kevin Denis [aut]
Maintainer: Philippe Grosjean <phgrosjean at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: mlearning results


Reference manual: mlearning.pdf


Package source: mlearning_1.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlearning_1.1.1.tgz, r-oldrel (arm64): mlearning_1.1.1.tgz, r-release (x86_64): mlearning_1.1.1.tgz, r-oldrel (x86_64): mlearning_1.1.1.tgz
Old sources: mlearning archive

Reverse dependencies:

Reverse depends: zooimage


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