What is Precision and Recall in Machine Learning?
Precision and Recall are evaluation metrics commonly used in classification tasks. Precision measures the proportion of correctly predicted positive instances out of the total predicted positive instances, while Recall measures the proportion of correctly predicted positive instances out of the total actual positive instances. Precision emphasizes the accuracy of positive predictions, while Recall focuses on the coverage of positive instances.
