Area under ROC curve (or AUC) provides an aggregate measure of performance across all possible classification thresholds.
It is the probability that the model will rank a randomly chosen positive example more highly than a randomly chosen negative example.
AUC ranges in value from 0.0 to 1.0.
- AUC of 0← 100% wrong
- AUC of 1 ← 100% correct
An AUC smaller than 0.5 indicates that the model performs worse than a random classifier (i.e. a classifier that randomly assigns each example to True or False), and an AUC larger than 0.5 indicates that the model performs better than a random classifier.