Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020. <doi:10.24963/ijcai.2020/366>.
| Version: | 0.1.4 |
| Depends: | R (≥ 3.5.0) |
| Imports: | caret, randomForest, stats |
| Suggests: | CORElearn |
| Published: | 2020-09-23 |
| Author: | Andre Maletzke [aut, cre], Everton Cherman [ctb], Denis dos Reis [ctb], Gustavo Batista [ths] |
| Maintainer: | Andre Maletzke <andregustavom at gmail.com> |
| BugReports: | https://github.com/andregustavom/mlquantify/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| URL: | https://github.com/andregustavom/mlquantify |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | mlquantify results |
| Reference manual: | mlquantify.pdf |
| Package source: | mlquantify_0.1.4.tar.gz |
| Windows binaries: | r-devel: mlquantify_0.1.4.zip, r-release: mlquantify_0.1.4.zip, r-oldrel: mlquantify_0.1.4.zip |
| macOS binaries: | r-release: mlquantify_0.1.4.tgz, r-oldrel: mlquantify_0.1.4.tgz |
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