Recall Optimization

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Recall optimization focuses on reducing missed detections. It matters in tasks where failing to catch a true case is expensive or dangerous. A simple example is fraud detection. Missing a fraudulent transaction can cost real money, so the system is tuned to catch as many true fraud cases as possible, even if that means it flags more transactions for review.

Improving recall usually involves changing how the model learns or how its outputs are used. Teams may add more examples of the rare cases, correct labels that hide true positives, or adjust the decision threshold so the system is more willing to flag a case. The trade-off is that higher recall often brings more false alarms, which can overload reviewers or annoy users.

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