When should I use a PR curve?

What is a PR curve and when should I use it?

A PR curve, or Precision-Recall curve, is a graphical representation of a model's precision and recall at different thresholds. It's particularly useful for evaluating models on imbalanced datasets.

Why is it better than using ROC curves?

PR curves focus on the performance of the positive class, making them more informative than ROC curves when dealing with imbalanced datasets.

Can you give me an example of when to use a PR curve?

Sure! If you're working on a medical diagnosis model where positive cases (e.g., patients with a disease) are much fewer than negative cases, a PR curve would help you understand how well your model identifies those rare positive cases.

How do I interpret the area under the PR curve?

The area under the PR curve (AUC-PR) indicates the model's ability to distinguish between classes. A higher AUC-PR value means better performance in identifying positive instances.