# ROC-AUC

> Source: https://sukruyusufkaya.com/en/glossary/roc-auc
> Updated: 2026-05-13T20:57:46.117Z
> Type: glossary
> Category: matematik-istatistik-optimizasyon
**TLDR:** A widely used comparison metric that summarizes a classifier’s ability to separate positives and negatives across thresholds.

<p>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.</p>