ROC-Optimizing Binary Classifiers
Overview
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).
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Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
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- ERROR11 OK, 1 ERROR2026-04-25
- OK14 OK2026-03-10
Documentation
- Help pages
- 17 (1 internal)
- Examples that run
- 100%15 run in a plain check
- Exported functions with examples
- 83%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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Dependencies
Installing roclab adds 70 packages (37 with compiled code, 52 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
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- RR 4.6.0 released · 2026-04-24
- 0.1.4Latest
- 0.1.32025-10-28
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-10-28
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 101 KB
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