What are precision and recall in machine learning?

What are precision and recall in machine learning?

Precision and recall are metrics used to evaluate the performance of classification models. Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances.

Can you explain how to calculate precision and recall?

Sure! Precision is calculated as the number of true positives divided by the sum of true positives and false positives. Recall is calculated as the number of true positives divided by the sum of true positives and false negatives.

Why are these metrics important?

They are crucial for understanding the trade-offs between false positives and false negatives, especially in applications like medical diagnosis or fraud detection.

Can you give an example of when to prioritize precision over recall?

Certainly! In spam detection, you might prioritize precision to ensure that legitimate emails aren't incorrectly marked as spam.