How to assess quality and correctness of classification models? Part 4 - ROC Curve

In this fourth part of the tutorial we will discuss the ROC curve. The ROC curve is one of the methods for visualizing classification quality, which shows the dependency between TPR (True Positive Rate) and FPR (False Positive Rate). The more convex the curve, the better the classifier. In the example below, the „green" classifier is better in area 1, and the „red" classifier is better in area 2. AUC 1 means a perfect classifier, AUC 0.5 is obtained for purely random classifiers. AUC 0.5 means the classifier performs wor