seniorSupervised Learning
What is ROC-AUC and why is it important?
Updated May 17, 2026
Short answer
ROC-AUC measures how well a classifier distinguishes between classes across thresholds.
Deep explanation
ROC curve plots True Positive Rate vs False Positive Rate at different thresholds. AUC measures the area under this curve, representing separability. A model with AUC = 1 is perfect, while 0.5 is random guessing.
Real-world example
Used in medical diagnosis to evaluate disease detection models.
Common mistakes
- Confusing ROC-AUC with accuracy.
Follow-up questions
- What is PR-AUC?
- When is ROC-AUC misleading?