What is a precision-recall curve?

What is a precision-recall curve?

A precision-recall curve is a graphical representation that shows the trade-off between precision and recall for different thresholds.

Why is it important to use a precision-recall curve?

It's important because it helps evaluate the performance of a model, especially in imbalanced datasets, by showing how well it can identify positive instances.

How do I interpret the curve?

The closer the curve is to the top-right corner, the better the model's performance. A high area under the curve indicates a good balance between precision and recall.

Can you give an example of when to use it?

Sure! It's commonly used in binary classification tasks, like spam detection, where you want to minimize false positives and false negatives.