The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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 |
| 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_0.2.0.tar.gz |
| Windows binaries: | r-devel: mlquantify_0.2.0.zip, r-release: mlquantify_0.2.0.zip, r-oldrel: mlquantify_0.2.0.zip |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=mlquantify to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.