roclab: ROC-Optimizing Binary Classifiers
Implements ROC (Receiver Operating Characteristic)–Optimizing Binary Classifiers, supporting both linear and kernel models. Both model types provide a variety of surrogate loss functions. In addition, linear models offer multiple regularization penalties, whereas kernel models support a range of kernel functions. Scalability for large datasets is achieved through approximation-based options, which accelerate training and make fitting feasible on large data. Utilities are provided for model training, prediction, and cross-validation. The implementation builds on the ROC-Optimizing Support Vector Machines. For more information, see Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>, presented in the ROC Analysis in AI Workshop (ROCAI-2004).
| Version: | 0.1.4 |
| Imports: | stats, graphics, utils,ggplot2,fastDummies,kernlab,pracma,rsample,dplyr,caret,pROC |
| Suggests: | mlbench,knitr,rmarkdown,testthat (≥ 3.0.0) |
| Published: | 2025-11-04 |
| DOI: | 10.32614/CRAN.package.roclab |
| Author: | Gimun Bae [aut, cre], Seung Jun Shin [aut] |
| Maintainer: | Gimun Bae <gimunbae0201 at gmail.com> |
| BugReports: | https://github.com/gimunBae/roclab/issues |
| License: | MIT + fileLICENSE |
| URL: | https://github.com/gimunBae/roclab |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | roclab results |
Documentation:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=roclabto link to this page.