What are precision and recall in machine learning?

Can you explain what precision and recall mean in machine learning?

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

Can you give me an example to illustrate the difference?

Of course! Imagine a spam filter: if it identifies 8 spam emails correctly but marks 2 legitimate emails as spam, precision is 8/(8+2) = 0.8. If there are 10 spam emails total, recall is 8/10 = 0.8.

How do precision and recall affect model evaluation?

They help assess the model's performance, especially in imbalanced datasets. High precision means fewer false positives, while high recall means fewer false negatives.