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Specificity and Binary Classification Probabilities

Summarize binary classification counts and use a specified prevalence to calculate predictive probabilities.

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How to use this calculator

  1. Enter the known values in the units shown. Results update as you type.
  2. Where results are editable, change one to solve backwards. Lock a value to hold it fixed.
  3. Use the worked example to check the method. Reset restores the starting fields.

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Formula and method

Summarize binary classification counts and use a specified prevalence to calculate predictive probabilities.

sensitivity=TP/(TP+FN); specificity=TN/(TN+FP); predictive values follow Bayes’ rule

Worked example

Enter these known values and leave the other values blank.

True positives
40
True negatives
40
False positives
10
False negatives
10
Prevalence
50 %
Specificity
80 %
Sensitivity
80 %
Positive likelihood ratio
4
Negative likelihood ratio
0.25
Accuracy
80 %
PPV (positive predictive value)
80 %
NPV (negative predictive value)
80 %

Assumptions and limitations

  • This is hypothetical binary classification arithmetic. It does not diagnose an individual or establish that a test or classifier is suitable for a setting.
  • Counts are nonnegative integers, with positive true-positive and true-negative counts in the retained model. Prevalence is the proportion with the condition in the target population.
  • Predictive values depend on the supplied prevalence. Changing prevalence need not alter sensitivity or specificity.
  • Singular likelihood-ratio endpoints cannot yield finite ratios; clearing a value leaves an incomplete calculation.

Common questions

How should I use an inverse?

Leave the required unknown empty and supply compatible known values. Multiple mathematical solutions or zero denominators can prevent a unique inverse.

What does this model assume?

This is hypothetical binary classification arithmetic. It does not diagnose an individual or establish that a test or classifier is suitable for a setting.

References

Replace explanatory external images with original accessible descriptions preserving every image condition and control relationship. Require integer classification counts so retained diagnostic count formulas cannot accept fractional people or observations.

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