Free · Probability and statistical models

Binary Classifier Sensitivity and Predictive Probabilities

Compute classification rates and likelihood ratios from a binary confusion table.

  • Formula & worked example
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Calculator inputs

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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.

Private by default

Inputs and results stay in this browser tab. Bookify does not upload or store the values you enter.

Formula and method

Forty correct positives and ten missed positives give 80% sensitivity. Forty correct negatives and ten false positives give 80% specificity.

Sensitivity = TP/(TP+FN); specificity = TN/(TN+FP)

Worked example

Enter these known values and leave the other values blank.

True positive
40
True negative
40
False positive
10
False negative
10
Prevalence
50 %
Sensitivity
80 %
Specificity
80 %
Positive likelihood ratio
4
Negative likelihood ratio
0.25
PPV
80 %
NPV
80 %
Accuracy
80 %

Assumptions and limitations

  • Counts are nonnegative whole values with nonzero positive and negative class totals.
  • Predictive probabilities depend on the supplied prevalence; examples are hypothetical binary classification arithmetic.
  • These summaries alone do not decide whether a classifier or test is appropriate for an individual or setting.
  • The retained model requires positive true-positive and true-negative counts and positive prevalence and predictive probabilities; singular endpoint cases are outside this model.

Common questions

Which assumptions govern this model?

Counts are nonnegative whole values with nonzero positive and negative class totals.

References

Replace captured external image with an original accessible description preserving its complete probability/count relationships and all variant conditions. Enforce the mathematical input domain and include valid zero boundaries while preserving the captured arithmetic and control graph.

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