How do I calculate precision and recall in machine learning?

How do I calculate precision and recall in machine learning?

To calculate precision and recall, you need to understand the concepts of true positives, false positives, and false negatives.

Can you explain what true positives and false positives are?

Sure! True positives are the cases where the model correctly predicts the positive class, while false positives are the cases where it incorrectly predicts the positive class.

And how do I use these to calculate precision?

Precision is calculated as the number of true positives divided by the sum of true positives and false positives: Precision = TP / (TP + FP).

What about recall? How is it calculated?

Recall is calculated as the number of true positives divided by the sum of true positives and false negatives: Recall = TP / (TP + FN).