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precision

Of everything a model flagged as positive, the fraction that was actually correct.

Precision measures, of everything a model flagged as a positive prediction, the fraction that was actually correct: \text{Precision}=TP/(TP+FP). Low precision means the model is raising too many false alarms.

Examples

  • A spam filter that flags 100 emails, 90 of them genuinely spam, has precision =90/100=0.9.

See also

recall F1 score
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