mlearning: Machine Learning Algorithms with Unified Interface and Confusion
Matrices
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 |
| 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 |
Documentation:
Downloads:
Reverse dependencies:
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