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EnglishThe 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.
| Titolo: | ROSE: a Package for Binary Imbalanced Learning |
| Autori: | |
| Data di pubblicazione: | 2014 |
| Rivista: | |
| 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. |
| Handle: | http://hdl.handle.net/11368/2787725 |
| URL: | http://journal.r-project.org/archive/2014-1/menardi-lunardon-torelli.pdf |
| Appare nelle tipologie: | 1.1 Articolo in Rivista |
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