mlearning: Machine Learning Algorithms with Unified Interface and Confusion
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.
||R (≥ 3.0.4)
||stats, grDevices, class, nnet, MASS, e1071, randomForest, ipred
||mlbench, datasets, RColorBrewer
||Philippe Grosjean [aut, cre],
Kevin Denis [aut]
||Philippe Grosjean <phgrosjean at sciviews.org>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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