What are ROC and PR curves in evaluating model performance?

What are ROC and PR curves in evaluating model performance?

ROC (Receiver Operating Characteristic) curves plot the true positive rate against the false positive rate. PR (Precision-Recall) curves plot precision against recall. Both are used to evaluate the performance of classification models.

How do I interpret the area under the ROC curve?

The area under the ROC curve (AUC) represents the model's ability to distinguish between classes. An AUC of 0.5 indicates no discrimination, while an AUC of 1.0 indicates perfect discrimination.

When should I use PR curves instead of ROC curves?

PR curves are more informative when dealing with imbalanced datasets, as they focus on the performance of the positive class.

Can you give an example of how to calculate these metrics?

Sure! To calculate the AUC for ROC, you can use the trapezoidal rule on the ROC curve. For PR curves, you calculate precision and recall at different thresholds and plot them.