Area under the ROC curve
The area under an ROC curve.
A number between 0.0 and 1.0 representing a binary classification model’s ability to separate positive classes from negative classes. The closer the AUC is to 1.0, the better the model’s ability to separate classes from each other.1
AUC ignores any value you set for classification threshold. Instead, AUC considers all possible classification thresholds.
Formal definition
AUC is the probability that a classifier will be more confident that a randomly chosen positive example is actually positive than that a randomly chosen negative example is positive.1
Examples
A spam classifier with AUC of 1.0 always assigns a random spam email a higher probability of being spam than a random legitimate email. The actual classification of each email depends on the threshold that you choose.