Random Over-Sampling Examples
Metrics to evaluate a classifier accuracy in imbalanced learning
Over-sampling, under-sampling, combination of over- and under-sampling...
ROC curve
ROSE: Random Over-Sampling Examples
Evaluation of learner accuracy by ROSE
Generation of synthetic data by Randomly Over Sampling Examples (ROSE)
Functions to deal with binary classification problems in the presence of imbalanced classes. Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). Functions that implement more traditional remedies to the class imbalance are also provided, as well as different metrics to evaluate a learner accuracy. These are estimated by holdout, bootstrap or cross-validation methods.