What are precision, recall, and accuracy in machine learning?

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

Sure! Precision is the ratio of true positive predictions to the total positive predictions. Recall is the ratio of true positives to the actual positives, and accuracy is the ratio of all correct predictions to the total predictions.

Can you give me an example to illustrate these concepts?

Absolutely! Imagine a model that predicts whether emails are spam. If it identifies 8 spam emails correctly (true positives) but also marks 2 legitimate emails as spam (false positives), the precision would be 8/(8+2) = 0.8 or 80%.

What about recall in this example?

In the same example, if there were 10 actual spam emails and the model identified 8 of them, the recall would be 8/(10) = 0.8 or 80% as well.

And how do we calculate accuracy?

For accuracy, if the model made 8 correct predictions (8 true positives) and 2 incorrect ones (2 false positives), plus 90 correct non-spam predictions, the accuracy would be (8 + 90) / (8 + 2 + 90) = 98%.