ROC analysis investigates and employs the relationship between sensitivity and specificity
of a binary classifier. Sensitivity or true positiverate measures the proportion of positives …

Objectives▪ After this tutorial, you will be able to▪[model evaluation] produce ROC plots for
categorical and ranking classifiers and calculate their AUC; apply crossvalidation in doing …

ROC analysis is increasingly being recognised as an important tool for evaluation and
comparison of classifiers when the operating characteristics (ie class distribution and cost …

We counsel caution in the application of ROC analysis for prediction of classifier
performance under varying class distributions. We argue that it is not reasonable to expect …

This paper considers binary classification. We assess a classifier in terms of the area under
the ROC curve (AUC). We estimate three important parameters, the conditional AUC …

The area under the ROC curve (AUC) is a very widely used measure of performance for
classification and diagnostic rules. It has the appealing property of being objective, requiring …

When designing a two-alternative classifier, one ordinarily aims to maximize the classifier's
ability to discriminate between members of the two classes. We describe a situation in a real …

This paper proposes an alternative to ROC representation, in which the expected cost of a
classifier is represented explicitly. This expected cost representation maintains many of the …

The use of ROC (Receiver Operating Characteristics) analysis as a tool for evaluating the
performance of classification models in machine learning has been increasing in the last …

In an article in this issue, Webb and Ting criticize ROC analysis for its inability to handle
certain changes in class distributions. They imply that the ability of ROC graphs to depict …