What are precision-recall curves and how do they work?

What are precision-recall curves and how do they work?

Precision-recall curves are graphical representations that show the trade-off between precision and recall for different thresholds in a classification model.

Can you explain what precision and recall mean?

Sure! Precision is the ratio of true positive predictions to the total predicted positives, while recall is the ratio of true positives to the total actual positives.

How do I interpret a precision-recall curve?

A precision-recall curve plots precision on the y-axis and recall on the x-axis. A curve closer to the top right corner indicates a better model.

What is the difference between precision-recall curves and ROC curves?

Precision-recall curves focus on the performance of a model on the positive class, while ROC curves consider both classes. They provide different insights.