Think Different

  Precision and recall are two metrics used to evaluate the performance of a classifier in binary classification problems. Precision measures the accuracy of positive predictions. It is calculated by dividing the number of true positives by the total number of positive predictions. Recall measures the completeness of positive predictions. It is calculated by dividing the number of true positives by the total number of actual positives. For example, let's say we have a classifier that predicts whether an email is spam or not. The classifier correctly predicts that 8 emails are spam and incorrectly predicts that 2 emails are not spam. There are actually 12 spam emails in the dataset. In this case, the precision of the classifier is 8/10 = 0.8, and the recall is 8/12 = 0.67. Precision is often used when the cost of false positives is high. For example, in the spam filtering example, a false positive would be an email that is incorrectly classified as spam. This could lead to th...