X.func.complete

<p>These helper functions are used only inside the package (not exported).
They handle pairwise difference construction, Adamax-based optimization,
proximal updates for penalties, intercept estimation, and lambda max.</p>

internal

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).

Gimun Bae

roclab

X.func.complete: utils-internal.R - Internal utilities for ROC-SVM

Description

Usage

Arguments