Technical GlossaryMathematics, Statistics and Optimization

Precision-Recall AUC

An evaluation metric that summarizes how well a model retrieves useful positives, especially in imbalanced settings.

Precision-Recall AUC can provide a more meaningful comparison than ROC-AUC when the positive class is rare. It emphasizes how well the model retrieves truly useful positives and how well it does so without generating too many false alarms. In domains such as fraud detection, disease screening, anomaly detection, and risk modeling, this metric often provides more realistic insight. When classes are imbalanced, the precision-recall perspective is frequently more valuable for understanding model performance.