Specificity Calculator
Calculate specificity (true negative rate) from true negative and false positive counts.
Inputs
Specificity (TNR)
0.98%
Actual Negatives (TN + FP)
870
Step by step
Specificity: TN / (TN + FP)
850 ÷ (850 + 20)
= 0.9770
How it works
Specificity, also called the true negative rate (TNR), measures how many of the actual negative cases the model correctly identified as negative: specificity = TN / (TN + FP). High specificity means few false alarms among true negatives. It's the mirror image of sensitivity/recall (which focuses on the positive class), and the pair together fully describe a binary classifier's behavior on both classes — this pair is exactly what's plotted on the ROC curve.
Formula
specificity = TN / (TN + FP)
- TN
- True negatives
- FP
- False positives
Frequently Asked Questions
How is specificity different from precision?
Specificity measures correctness among actual negatives (TN / (TN+FP)), while precision measures correctness among predicted positives (TP / (TP+FP)) — they use different denominators and answer different questions.
When is high specificity most important?
High specificity matters most when false positives are costly or disruptive, such as flagging healthy patients for invasive follow-up testing, or blocking legitimate users as fraudulent.
How is specificity related to the false positive rate?
False positive rate (FPR) = 1 − specificity, so they're direct complements — the ROC curve's x-axis (FPR) is simply one minus this calculator's output.