Glossary › AUC-ROC
AUC-ROC (Area Under the Receiver Operating Characteristic Curve)
Measures how well a binary classifier separates positive and negative classes across all thresholds. A value of 1.0 is perfect; 0.5 is random. Useful for comparing models when you have not yet committed to a threshold. For severely imbalanced data (fraud, rare disease), prefer AUC-PR instead.