Precision Calculator
Calculate precision from true positives and false positives in a classification model.
About Precision Calculator
Precision, also called positive predictive value, measures how many of the predictions labeled positive were actually correct: precision = TP / (TP + FP). High precision means few false alarms — when the model says 'positive', it's usually right. Precision says nothing about the positives it missed (false negatives); pair it with recall to see the full picture, especially when the cost of a false positive (e.g. flagging a legitimate transaction as fraud) is high.
Formula
precision = TP / (TP + FP)
- TP
- True positives
- FP
- False positives
Worked example
With the default values below, Precision Calculator returns Precision of 0.80%. Change any input above and every figure here is recalculated in your browser.
Inputs used
- True Positives (TP)accepts 0–1,000,000,000
- 80
- False Positives (FP)accepts 0–1,000,000,000
- 20
Results
- Precision
- 0.80%
- Predicted Positives (TP + FP)
- 100
Step by step
- 1Precision: TP / (TP + FP)= 0.8000
Assumptions and limits
- The result is derived only from the 2 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
What does low precision indicate?
Low precision means a large share of the model's positive predictions are actually false positives, so the model is 'crying wolf' too often relative to how many of its positive calls are correct.
When should I prioritize precision over recall?
Prioritize precision when false positives are costly — for example, flagging legitimate emails as spam, or approving a loan application incorrectly — where you'd rather miss some true positives than act on wrong ones.
Can precision be high while recall is very low?
Yes — a model that only predicts positive when extremely confident can have near-perfect precision but miss most actual positives, resulting in very low recall; this tradeoff is why both metrics are usually reported together.