Hands-On Machine Learning with C++

AUC–ROC

Usually, a classification algorithm will not return a concrete class identifier but a probability of an object belonging to some class. So, we usually use a threshold to decide whether an object belongs to a class or not. The most apparent threshold is 0.5, but it can work incorrectly in the case of imbalanced data (when we have a lot of values for one class and significantly fewer for other class).

One of the methods we can use to estimate a model without the actual threshold is the value of the Area Under Receiver Operating Characteristic curve (AUC-ROC). This curve is a line from (0,0) to (1,1) in coordinates of the True Positive Rate (TPR) and the False Positive Rate (FPR):

The TPR is equal to the recall, while the FPR value ...