Getting ready…

When we are dealing with machine learning models, we usually care about three things—precision, recall, and F1 score. We can get the required performance metric using parameter scoring. Precision refers to the number of items that are correctly classified as a percentage of the overall number of items in the list. Recall refers to the number of items that are retrieved as a percentage of the overall number of items in the training list.