Free · Probability and statistical models

Binary Classification Correlation and Rates

Compare binary predictions with observed classes using four confusion counts and a read-only correlation result.

  • 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 true positives, forty true negatives and ten of each error give MCC 0.6.

MCC = (TP TN − FP FN)/√((TP+FP)(TP+FN)(TN+FP)(TN+FN))

Worked example

Enter these known values and leave the other values blank.

True positives (TP)
40
True negatives (TN)
40
False positives (FP)
10
False negatives (FN)
10
Correlation coefficient
0.6
Sensitivity
80 %
Specificity
80 %
Precision
80 %
Accuracy
80 %
F1 score
80 %

Assumptions and limitations

  • Counts are nonnegative integers; each rate requires a nonzero denominator.
  • MCC is a correlation coefficient between −1 and 1. A zero denominator leaves it undefined.
  • MCC is a read-only result. Edit the counts or the other available rates; it cannot be entered as a manual target in this interface.
  • Classification rates summarize labeled data; they do not establish the usefulness of a model for a particular decision.

Common questions

Can I edit the correlation result?

The correlation result is read-only. Enter confusion counts, or edit an available rate to solve a compatible unknown count.

References

Require nonnegative whole confusion counts and valid classification-rate domains.

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