Compute AUC (Area Under the ROC Curve) for a fitted model.
Dispatches to class-specific methods such as <code>auc.roclearn</code>.

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

ROC-Optimizing Binary Classifiers

Seung Jun Shin

auc function

<dl><dt>object</dt>
<dd>A fitted model object.</dd>
<dt>...</dt>
<dd>Additional arguments passed to methods.</dd></dl>

Arguments

Generic function for AUC — auc

<dl>

<dt>object</dt>
<dd>A fitted model object.</dd>


<dt>...</dt>
<dd>Additional arguments passed to methods.</dd>

</dl>

auc: Generic function for AUC

Description

Usage

Value

Arguments