What is recall in machine learning?

Can you explain what recall means in machine learning?

Sure! Recall, also known as sensitivity, measures the ability of a model to identify all relevant instances. It's the ratio of true positives to the sum of true positives and false negatives.

How is recall different from precision?

Great question! While recall focuses on the true positives, precision measures the accuracy of the positive predictions. In other words, precision is the ratio of true positives to the total predicted positives.

Why is recall important in certain applications?

Recall is crucial in applications like medical diagnosis, where missing a positive case can have serious consequences. High recall ensures that most actual positives are identified.

Can you give an example of how to calculate recall?

Absolutely! If a model predicts 80 true positives, 10 false negatives, and 5 false positives, recall would be 80 / (80 + 10) = 0.89 or 89%.