50 correct out of 60 positive predictions
tp = 50fp = 10fn = 5tn = 35
Result: Precision 0.8333 · False discovery rate 0.1667
Precision Calculator
Precision answers "of all the times the model predicted positive, how often was it right?" It is TP ÷ (TP + FP), and it matters when false alarms are expensive — spam filters, fraud detection, medical screens. Enter your confusion-matrix counts to get precision and the false discovery rate.
Enter your values and press Calculate — the result appears here.
Worked examples
No false alarms
tp = 30fp = 0fn = 10tn = 60
Result: Precision 1.00
Frequently asked questions
What does precision tell me?
Precision is the reliability of a positive prediction: of everything the model flagged as positive, the fraction that was genuinely positive. High precision means few false alarms.
When is precision the right metric?
When a false positive is expensive: spam that blocks a real email, fraud alerts that freeze a legitimate card, or medical screenings that cause unnecessary anxiety. There you optimize precision, even at the cost of recall.
What is the false discovery rate?
The complement of precision (1 − precision): the share of positive predictions that were actually wrong. Precision 0.8333 means 16.67% of positive predictions were false alarms.
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