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AI & Machine Learning · Foundational

Recall

Of all actual positives, the fraction the model correctly identifies: TP / (TP + FN). Also called sensitivity or true positive rate.

Definition

Recall answers how many real positives were caught. It is prioritized when missing a positive is costly, such as disease screening, fraud detection or security alerts.

Exam tip

High cost of missed cases means optimize recall. Sensitivity = recall = TPR.

Don't confuse it with

Related terms