Technical GlossaryMathematics, Statistics and Optimization

ROC-AUC

A widely used comparison metric that summarizes a classifier’s ability to separate positives and negatives across thresholds.

ROC-AUC summarizes how well a classifier can separate positive and negative examples across different threshold values. Its strength lies in evaluating overall discriminative ability without committing to a single threshold. It is widely used to compare models that output scores or probabilities. However, in highly imbalanced settings, ROC-AUC can sometimes appear overly optimistic and should be interpreted with context. Even so, it remains one of the most established metrics in model comparison.