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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).

Versions across snapshots

VersionRepositoryFileSize
0.1.4 rolling linux/jammy R-4.5 roclab_0.1.4.tar.gz 243.1 KiB
0.1.4 rolling linux/noble R-4.5 roclab_0.1.4.tar.gz 243.3 KiB
0.1.4 rolling source/ R- roclab_0.1.4.tar.gz 100.9 KiB
0.1.4 latest linux/jammy R-4.5 roclab_0.1.4.tar.gz 243.1 KiB
0.1.4 latest linux/noble R-4.5 roclab_0.1.4.tar.gz 243.3 KiB
0.1.4 latest source/ R- roclab_0.1.4.tar.gz 100.9 KiB
0.1.4 2026-04-26 source/ R- roclab_0.1.4.tar.gz 100.9 KiB
0.1.4 2026-04-23 source/ R- roclab_0.1.4.tar.gz 100.9 KiB
0.1.4 2026-04-09 windows/windows R-4.5 roclab_0.1.4.zip 246.1 KiB

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