FNR Calculator
Calculate the false negative rate from a confusion matrix and classification results.
About FNR Calculator
The false negative rate (FNR), also called the miss rate, measures how often the model fails to catch a true positive: FNR = FN / (FN + TP) = 1 − sensitivity. It directly quantifies the 'missed detections' side of a classifier's error profile. FNR is especially critical in safety and screening contexts, where a missed positive (e.g. an undetected disease or security threat) can carry a much higher cost than a false alarm.
Formula
FNR = FN / (FN + TP)
- FN
- False negatives
- TP
- True positives
Worked example
With the default values below, FNR Calculator returns False Negative Rate of 0.11%. Change any input above and every figure here is recalculated in your browser.
Inputs used
- False Negatives (FN)accepts 0–1,000,000,000
- 10
- True Positives (TP)accepts 0–1,000,000,000
- 80
Results
- False Negative Rate
- 0.11%
- Sensitivity (1 − FNR)
- 0.89%
Step by step
- 1FNR: FN / (FN + TP)= 0.1111
- 2Sensitivity: 1 − FNR= 0.8889
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 FNR different from FPR?
FNR measures missed true positives (FN / (FN+TP)), the mirror of sensitivity, while FPR measures false alarms among true negatives (FP / (FP+TN)), the mirror of specificity — both quantify different error types on opposite classes.
Why is FNR sometimes more important than FPR?
In domains like disease screening, security threat detection, or safety-critical systems, a missed true positive (high FNR) can have far more severe consequences than a false alarm, so minimizing FNR is often prioritized even at the cost of a higher FPR.
Is FNR the same as the Type II error rate?
Yes — in classical hypothesis testing terms, FNR corresponds exactly to the Type II error rate (failing to reject a false null hypothesis, or here, missing a true positive).