Recall

Recall is defined as the fraction of positive cases that are correctly Identified.

It is a ratio of correct positive predictions to all overall number of positive instances in the dataset.

The formula to compute Recall is :

OR

For example:

Let us assume that you developed an AI model that tests pooled specimen(blood/urine etc.) to diagnose some ailment (say covid). After its training with sample collection of specimens whose accurate results were known to you, post testing, you are now ready to evaluate your AI model. For your AI model, you conduct about 630 tests and the confusion matrix with these 630 tests results looked like:

SOLUTION

As per the problem, we applied a formula to compute Recall as-

OR

Thus, the Recall for our sample AI model is 68.75 % or 0.6875.

Precision is used as a metric when our objective is to minimize False Positves and Recall is used when the objective is to minimize False Negatives.