Overview
AUC (specifically ROC-AUC) provides an aggregate measure of performance across all possible classification thresholds. It can be interpreted as the probability that the model will rank a random positive example higher than a random negative example.
Values
- 1.0: A perfect classifier.
- 0.5: A classifier that is no better than random guessing.
- 0.0: A classifier that is perfectly wrong (predicts the opposite of the truth).
Benefits
- Scale-Invariant: Measures how well predictions are ranked, rather than their absolute values.
- Classification-Threshold-Invariant: Measures the quality of the model's predictions regardless of what threshold is chosen.