The R Journal: article published in 2014, volume 6:1

ROSE: a Package for Binary Imbalanced Learning
Nicola Lunardon, Giovanna Menardi and Nicola Torelli , The R Journal (2014) 6:1, pages 79-89.

Abstract 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.

Received: 2013-09-21; online 2014-06-16
CRAN packages: DMwR, caret, ROSE, ROSE, ROSE, class
CRAN Task Views implied by cited CRAN packages: Multivariate, HighPerformanceComputing, MachineLearning, SocialSciences

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2014-008,
  author = {Nicola Lunardon and Giovanna Menardi and Nicola Torelli},
  title = {{ROSE: a Package for Binary Imbalanced Learning}},
  year = {2014},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2014-008},
  url = {https://doi.org/10.32614/RJ-2014-008},
  pages = {79--89},
  volume = {6},
  number = {1}
}