The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. Artificial balanced samples are generated according to a smoothed bootstrap approach and allow for aiding both the phases of estimation and accuracy evaluation of a binary classifier in the presence of a rare class. Functions that implement more traditional remedies for the class imbalance and different metrics to evaluate accuracy are also provided. These are estimated by holdout, bootstrap or cross-validation methods.
DMwR, caret, ROSE, ROSE, ROSE, class
Multivariate, HighPerformanceComputing, MachineLearning, SocialSciences
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For attribution, please cite this work as
Lunardon, et al., "The R Journal: ROSE: a Package for Binary Imbalanced Learning", The R Journal, 2014
BibTeX citation
@article{RJ-2014-008,
author = {Lunardon, Nicola and Menardi, Giovanna and Torelli, Nicola},
title = {The R Journal: ROSE: a Package for Binary Imbalanced Learning},
journal = {The R Journal},
year = {2014},
note = {https://doi.org/10.32614/RJ-2014-008},
doi = {10.32614/RJ-2014-008},
volume = {6},
issue = {1},
issn = {2073-4859},
pages = {79-89}
}