What is Recall?
Recall
Recall (Sensitivity or True Positive Rate) is a classification evaluation metric measuring the fraction of actual positive examples that the model correctly identified, calculated as true positives divided by all actual positives.
Why It Matters for AI Builders
Serves as a vital benchmark for quality control in medical diagnosis screening, search indexing, and threat detection audits; analyzing Recall helps developers audit model behaviors and maintain production predictability.
Detailed Deep Dive
Recall (or sensitivity) is a performance metric that measures the proportion of correctly predicted positive instances out of all actual positive instances (true positives divided by true positives plus false negatives). It evaluates model coverage, indicating how well the model avoids false negatives, crucial for medical diagnostics.
Frequently Asked Questions
Q:What is the formula for recall?
`Recall = True Positives / (True Positives + False Negatives)`.
Q:When should recall be prioritized over precision?
When missing a positive event is extremely dangerous. For example, in cancer screening, a false negative (failing to detect cancer) is life-threatening, so the model must maximize recall.
Recall Media Coverage & Intelligence
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