Machine Learning with Spark - Second Edition

ROC curve and AUC

The ROC curve is a concept similar to the PR curve. It is a graphical illustration of the true positive rate against the false positive rate for a classifier.

The true positive rate (TPR) is the number of true positives divided by the sum of true positives and false negatives. In other words, it is the ratio of true positives to all positive examples. This is the same as the recall we saw earlier, and is also commonly referred to as sensitivity.

The false positive rate (FPR) is the number of false positives divided by the sum of false positives and true negatives (that is, the number of examples correctly predicted as class 0). In other words, it is the ratio of false positives to all negative examples.

In a manner similar ...