The receiver operating characteristics (ROC) curve allows us to visualize, organize, and select classifiers based on their performance. It computes all the combinations of true positive rates (TPR) and false positive rates (FPR) that result from producing predictions using any of the predicted scores as a threshold. It then plots these pairs on a square, the side of which has a measurement of one in length.

A classifier that makes random predictions (taking into account class imbalance) will on average yield TPR and FPR that are equal so that the combinations will lie on the diagonal, which becomes the benchmark case. Since an underperforming classifier would benefit from relabeling the predictions, this benchmark also becomes the minimum.

The area under the curve (AUC) is defined as the area under the ROC plot that varies between 0.5 and the maximum of 1. It is a summary measure of how well the classifier's scores are able to rank data points with respect to their class membership. More specifically, the AUC of a classifier has the important statistical property of representing the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance, which is equivalent to the Wilcoxon ranking test. In addition, the AUC has the benefit of not being sensitive to class imbalances.