Performance & Evaluation
Recall
The proportion of actual positives that the model correctly identifies.
Definition
Recall answers the question: 'Of all the true positives that exist, how many did the model catch?' If there are 100 fraudulent transactions and a fraud detection system flags 80 of them, its recall is 80%. High recall means few cases are missed. In high-stakes applications — medical diagnosis, fraud detection, safety monitoring — high recall (catching everything) may be more important than precision (avoiding false alarms). The right trade-off depends on the relative cost of missing a case versus investigating a false alarm.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026