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.

Lunardon, N., Menardi, G., Torelli, N. (2014). ROSE: A package for binary imbalanced learning. THE R JOURNAL, 6(1), 79-89 [10.32614/rj-2014-008].

ROSE: A package for binary imbalanced learning

LUNARDON, NICOLA
Primo
;
2014

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.
Articolo in rivista - Articolo scientifico
Statistics and Probability; Numerical Analysis; Statistics, Probability and Uncertainty
English
79
89
11
Lunardon, N., Menardi, G., Torelli, N. (2014). ROSE: A package for binary imbalanced learning. THE R JOURNAL, 6(1), 79-89 [10.32614/rj-2014-008].
Lunardon, N; Menardi, G; Torelli, N
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/142186
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