The package provides 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.
|Author||Nicola Lunardon, Giovanna Menardi, Nicola Torelli|
|Date of publication||2014-07-15 18:41:03|
|Maintainer||Nicola Lunardon <firstname.lastname@example.org>|
accuracy.meas: Metrics to evaluate a classifier accuracy in imbalanced...
hacide: Half circle filled data
ovun.sample: Over-sampling, under-sampling, combination of over- and...
roc.ROSE: ROC curve
ROSE: Generation of synthetic data by Randomly Over Sampling...
ROSE.eval: Evaluation of learner accuracy by ROSE
ROSE-package: ROSE: Random Over-Sampling Examples