NPV Calculator
Calculate the negative predictive value from a confusion matrix.
About NPV Calculator
Negative Predictive Value (NPV) measures how many of the predictions labeled negative were actually correct: NPV = TN / (TN + FN). It answers 'if the model says negative, how likely is that true?' — the mirror image of precision (which answers the same question for positive predictions). NPV is heavily influenced by prevalence: even a highly sensitive test can have low NPV in a population where the condition is very common, because false negatives become relatively more likely.
Formula
NPV = TN / (TN + FN)
- TN
- True negatives
- FN
- False negatives
Worked example
With the default values below, NPV Calculator returns Negative Predictive Value of 0.99%. Change any input above and every figure here is recalculated in your browser.
Inputs used
- True Negatives (TN)accepts 0–1,000,000,000
- 850
- False Negatives (FN)accepts 0–1,000,000,000
- 10
Results
- Negative Predictive Value
- 0.99%
- Predicted Negatives (TN + FN)
- 860
Step by step
- 1NPV: TN / (TN + FN)= 0.9884
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 NPV different from specificity?
Specificity = TN / (TN + FP) measures correctness among actual negatives, while NPV = TN / (TN + FN) measures correctness among predicted negatives — different denominators, and NPV is more sensitive to the underlying prevalence.
Why does NPV depend on prevalence?
When a condition is rare, most negative predictions really are negative, boosting NPV; when a condition is common, more of the 'negative' predictions turn out to be false negatives, lowering NPV even with the same sensitivity and specificity.
What's the ML equivalent term for NPV?
NPV doesn't have as common a machine-learning-specific renaming as sensitivity/specificity do (recall/TPR, TNR), but it's sometimes referred to as 'negative class precision' by analogy to standard precision for the positive class.