What is the difference between ROC curve and PR curve?

What is the difference between ROC curve and PR curve?

The ROC curve (Receiver Operating Characteristic) shows the trade-off between true positive rate and false positive rate. In contrast, the PR curve (Precision-Recall) focuses on the trade-off between precision and recall, especially useful for imbalanced datasets.

When should I use the ROC curve instead of the PR curve?

Use the ROC curve when you have a balanced dataset and want to evaluate the overall performance of a classifier. The PR curve is more informative for imbalanced datasets, where the positive class is rare.

Can you explain how to interpret these curves?

Sure! In the ROC curve, a curve closer to the top-left corner indicates better performance. For the PR curve, a curve closer to the top-right corner signifies better precision and recall.

Are there any specific metrics derived from these curves?

Yes! From the ROC curve, you can derive the AUC (Area Under the Curve), which quantifies the overall performance. From the PR curve, you can calculate average precision, which summarizes the precision-recall trade-off.