Recall

A metric for classification models that answers the following question:

When ground truth was the positive class, what percentage of predictions did the model correctly identify as the positive class?

Here is the formula:

where:

true positive means the model correctly predicted the positive class.

false negative means that the model mistakenly predicted the negative class.

For instance, suppose your model made 200 predictions on examples for which ground truth was the positive class. Of these 200 predictions:

180 were true positives.

20 were false negatives.

In this case:

Click the icon for notes about class-imbalanced datasets.

Created for this library

1.
A medical screening team monitors recall on its early-stage cancer classifier because missed positives carry high clinical cost.
2.
A cybersecurity team monitors recall on its intrusion detector so missed attacks remain rare even if it costs some precision.
3.
A retention team monitors recall on at-risk customers so the daily call list covers most of the highest-risk segment.