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Commonly confused · AI & Machine Learning

Precision vs Recall: What's the Difference?

Precision measures how many predicted positives are actually positive, TP / (TP + FP). Recall measures how many actual positives the model found, TP / (TP + FN). Improving one often lowers the other, and the F1 score balances both.

PrecisionRecall
FormulaTP / (TP + FP)TP / (TP + FN)
Question answeredWhen the model says yes, how often is it right?Of all real positives, how many did we catch?
PenalizesFalse positivesFalse negatives
Prioritize whenFalse alarms are costly (spam filtering, legal review)Missing a positive is costly (cancer screening, fraud, intrusion detection)
Also calledPositive predictive valueSensitivity, true positive rate
Remember it

Precision avoids false alarms; recall avoids misses.

Exam tip

Scenario questions describe the cost of errors: if missing a case is dangerous, choose recall. Accuracy is misleading on imbalanced datasets, which is why these metrics matter.

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