What is precision in classification?

Can you explain what precision means in classification?

Sure! Precision is the ratio of true positive predictions to the total positive predictions made by the model.

Why is precision important in evaluating a model?

Precision is crucial when the cost of false positives is high, as it helps to minimize incorrect positive predictions.

How does precision differ from recall?

Precision focuses on the accuracy of positive predictions, while recall measures the ability to identify all relevant instances.

Can you give an example of precision in a real-world scenario?

Certainly! In email filtering, precision would measure how many of the emails marked as spam are actually spam.