mlquantify: Algorithms for Class Distribution Estimation

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: 0.2.0
Imports: caret, randomForest, stats, FNN
Suggests: CORElearn
Published: 2022-01-20
DOI: 10.32614/CRAN.package.mlquantify
Author: Andre Maletzke [aut, cre], Everton Cherman [ctb], Denis dos Reis [ctb], Gustavo Batista [ths]
Maintainer: Andre Maletzke <andregustavom at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
NeedsCompilation: no
Materials: README
CRAN checks: mlquantify results


Reference manual: mlquantify.pdf


Package source: mlquantify_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlquantify_0.2.0.tgz, r-oldrel (arm64): mlquantify_0.2.0.tgz, r-release (x86_64): mlquantify_0.2.0.tgz, r-oldrel (x86_64): mlquantify_0.2.0.tgz
Old sources: mlquantify archive


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