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 + file LICENSE |
| URL: |
https://github.com/gimunBae/roclab |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
roclab results |
Documentation:
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