Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIA™ Study Guide recommends using precision when the goal is to minimize incorrect positive alerts, which is especially relevant in fraud detection, cybersecurity, and classification models.
“Precision is the key metric when false positives have a significant operational cost. It provides insight into the model’s ability to avoid incorrect positive classifications.”
Accuracy and recall give broader insights, but only precision directly measures false positive risk. Completeness is not a standard ML metric.
[Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI Operations and Performance,” Subsection: “Performance Metrics for Classification Models”, , ]
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