ROC-Optimizing Binary Classifiers
Compute AUC for a fitted kernel model
Generic function for AUC
Compute AUC for a fitted linear model
Cross-validation for kernel models
Cross-validation for linear models
Fit a kernel model
Plot Receiver Operating Characteristic (ROC) curve using ggroc
Visualize Cross-Validation results for kernel models
Visualize Cross-Validation results for linear models
Predictions from a fitted kernel model
Predictions from a fitted linear model
Fit a linear model
Summarize Cross-Validation results for kernel models
Summarize Cross-Validation results for linear models
Summarize a fitted kernel model
Summarize a fitted linear model
utils-internal.R - Internal utilities for ROC-SVM
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).