FPR Calculator
Calculate the false positive rate from a confusion matrix and classification results.
About FPR Calculator
The false positive rate (FPR) measures how often the model incorrectly flags a true negative as positive: FPR = FP / (FP + TN) = 1 − specificity. It's the x-axis of the ROC curve, representing the 'cost' side of the sensitivity/specificity tradeoff — as a model's decision threshold is lowered to catch more true positives, FPR typically rises as well, since more true negatives get swept up as false alarms.
Formula
FPR = FP / (FP + TN)
- FP
- False positives
- TN
- True negatives
Worked example
With the default values below, FPR Calculator returns False Positive Rate of 0.02%. Change any input above and every figure here is recalculated in your browser.
Inputs used
- False Positives (FP)accepts 0–1,000,000,000
- 20
- True Negatives (TN)accepts 0–1,000,000,000
- 850
Results
- False Positive Rate
- 0.02%
- Specificity (1 − FPR)
- 0.98%
Step by step
- 1FPR: FP / (FP + TN)= 0.0230
- 2Specificity: 1 − FPR= 0.9770
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
How is FPR used in the ROC curve?
The ROC curve plots true positive rate (sensitivity) on the y-axis against false positive rate on the x-axis across every possible classification threshold, visualizing the full tradeoff between catching positives and generating false alarms.
What's a 'good' FPR?
It depends entirely on context — for high-stakes false alarms (e.g. locking out a legitimate user), you want FPR very close to 0; for low-stakes screening where follow-up review is cheap, a higher FPR may be acceptable in exchange for higher sensitivity.
Is FPR the same as the Type I error rate?
Yes — in classical statistical hypothesis testing terms, FPR is exactly the Type I error rate (rejecting a true null hypothesis, or here, flagging a true negative as positive).