ROSE: Random Over-Sampling Examples

Functions to deal with binary classification problems in the presence of imbalanced classes. Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). Functions that implement more traditional remedies to the class imbalance are also provided, as well as different metrics to evaluate a learner accuracy. These are estimated by holdout, bootstrap or cross-validation methods.

Version: 0.0-4
Suggests: MASS, nnet, rpart, tree
Published: 2021-06-14
DOI: 10.32614/CRAN.package.ROSE
Author: Nicola Lunardon, Giovanna Menardi, Nicola Torelli
Maintainer: Nicola Lunardon <nicola.lunardon at>
License: GPL-2
NeedsCompilation: no
Citation: ROSE citation info
Materials: ChangeLog
CRAN checks: ROSE results


Reference manual: ROSE.pdf


Package source: ROSE_0.0-4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ROSE_0.0-4.tgz, r-oldrel (arm64): ROSE_0.0-4.tgz, r-release (x86_64): ROSE_0.0-4.tgz, r-oldrel (x86_64): ROSE_0.0-4.tgz
Old sources: ROSE archive

Reverse dependencies:

Reverse imports: themis


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