What are some common misconceptions about AUC-ROC?

What are some common misconceptions about AUC-ROC?

One common misconception is that a higher AUC always means a better model, but it doesn't account for class imbalance.

Can you explain what AUC-ROC actually measures?

AUC-ROC measures the ability of a model to distinguish between classes across all classification thresholds.

Are there situations where AUC-ROC might be misleading?

Yes, in cases of severe class imbalance, AUC-ROC can give an overly optimistic view of model performance.

What should I consider instead of AUC-ROC?

Consider metrics like precision-recall curves, especially when dealing with imbalanced datasets.