ROSE: a Package for Binary Imbalanced Learning

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.

Nicola Lunardon , Giovanna Menardi , Nicola Torelli

CRAN packages used

DMwR, caret, ROSE, ROSE, ROSE, class

CRAN Task Views implied by cited packages

Multivariate, HighPerformanceComputing, MachineLearning, SocialSciences


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


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

  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 = {},
  doi = {10.32614/RJ-2014-008},
  volume = {6},
  issue = {1},
  issn = {2073-4859},
  pages = {79-89}