What is the difference between recall and precision in machine learning?

What is the difference between recall and precision in machine learning?

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

Can you give me an example of how recall and precision are calculated?

Sure! If you have 100 positive cases and your model correctly identifies 80 of them, the recall is 80/100 = 0.8. If it predicts 90 cases as positive but only 80 are actually positive, the precision is 80/90 = 0.89.

Why is it important to consider both recall and precision?

Considering both metrics helps to balance the trade-off between false positives and false negatives, which is crucial depending on the application.

In what scenarios would you prioritize recall over precision?

You would prioritize recall in scenarios like disease detection, where missing a positive case could have serious consequences.