OpenCV 4 with Python Blueprints - Second Edition

Recall

Recall is similar to precision in the sense that it measures the fraction of relevant instances that are retrieved (as opposed to the fraction of retrieved instances that are relevant). Thus, it will tell us the probability that we will not notice it for a given positive class (given sign).

In a classification task, the number of false negatives is the number of items that are not labeled as belonging to the positive class but should have been labeled.

Recall is the number of true positives divided by the sum of true positives and false negatives. In other words, out of all the pictures of cats in the world, recall is the fraction of pictures that have been correctly identified as pictures of cats.

Here is how to calculate recall ...