mlearning: Machine Learning Algorithms with Unified Interface and ConfusionMatrices
A unified interface is provided to various machine learning algorithms like linear or quadratic discriminant analysis, k-nearest neighbors, random forest, support vector machine, ... 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 processed 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: | 1.2.1 |
| Depends: | R (≥ 3.0.4) |
| Imports: | stats, grDevices,class,nnet,MASS,e1071,randomForest,ipred,rpart |
| Suggests: | mlbench, datasets,RColorBrewer,spelling,knitr,rmarkdown,covr |
| Published: | 2023-08-30 |
| DOI: | 10.32614/CRAN.package.mlearning |
| Author: | Philippe Grosjean [aut, cre], Kevin Denis [aut] |
| Maintainer: | Philippe Grosjean <phgrosjean at sciviews.org> |
| BugReports: | https://github.com/SciViews/mlearning/issues |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://www.sciviews.org/mlearning/ |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | NEWS |
| CRAN checks: | mlearning results |
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