Python: End-to-end Data Analysis

Computing precision, recall, and F1-score

In the Getting classification straight with the confusion matrix recipe, you learned that we can label classified samples as true positives, false positives, true negatives, and false negatives. With the counts of these categories, we can calculate many evaluation metrics of which we will cover four in this recipe, as given by the following equations:

These metrics range from zero to one, with zero being the worst theoretical score and one being the best. Actually, the worst score would be the one we get by random guessing. The best score in practice may be lower than one because in some cases we can only ...