In the case of classification algorithms, we use a confusion matrix, which gives us the performance of the learning algorithm. It is a square matrix that counts the number of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) outcomes.
True positive: The number of cases that were observed and predicted as 1.
False negative: The number of cases that were observed as 1 but predicted as 0.
False positive: The number of cases that were observed as 0 but predicted as 1.
True negative: The number of cases that were observed as 1 but predicted as 0.
It is the ability of a classifier to not label a sample that is negative as positive. The precision for an algorithm is calculated using the following formula:
This is useful in the case of email spam detection. In this scenario, we do not want any important emails to be detected as spam.