ROSE: a Package for Binary Imbalanced Learning

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Authors Nicola Lunardon, Giovanna Menardi, Nicola Torelli
Journal/Conference Name {R
Paper Category
Paper 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.
Date of publication 2014
Code Programming Language R
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