Mastering Machine Learning with scikit-learn - Second Edition

ROC AUC

A Receiver Operating Characteristic (ROC) curve, visualizes a classifier's performance. Unlike accuracy, the ROC curve is insensitive to datasets with unbalanced class proportions; unlike precision and recall, the ROC curve illustrates the classifier's performance for all values of the discrimination threshold. ROC curves plot the classifier's recall against its fall-out. Fall-out, or the false positive rate, is the number of false positives divided by the total number of negatives. It is calculated using the following:

AUC is the area under the ROC curve; it reduces the ROC curve to a single value that represents the expected performance ...