What are common mistakes when interpreting ROC curves?

What are common mistakes when interpreting ROC curves?

Some common mistakes include misinterpreting the area under the curve (AUC) and not considering the context of the data.

Can you explain what AUC means in this context?

AUC stands for Area Under the Curve, which measures the model's ability to distinguish between classes. A higher AUC indicates better performance.

What should I consider when comparing ROC curves from different models?

When comparing ROC curves, consider the AUC, the shape of the curves, and the specific thresholds relevant to your application.

Are there any specific thresholds I should focus on?

Yes, focus on the threshold that maximizes sensitivity and specificity for your specific use case, as it varies by application.