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Matthews Correlation Coefficient (MCC) measures the quality of a classification by taking true and false positives and negatives into account. It is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. A coefficient of 1 represents a perfect prediction, 0 no better than random prediction, and −1 indicates total disagreement between prediction and observation.

\[ {\displaystyle \mathrm {MCC} = {\frac {\mathrm {TP} \times \mathrm {TN} -\mathrm {FP} \times \mathrm {FN} }{\sqrt {(\mathrm {TP} +\mathrm {FP} )(\mathrm {TP} +\mathrm {FN} )(\mathrm {TN} +\mathrm {FP} )(\mathrm {TN} +\mathrm {FN} )}}}} \]

Estimator Compatibility: Classifier, Anomaly Detector

Score Range: -1 to 1


This metric does not have any parameters.


use Rubix\ML\CrossValidation\Metrics\MCC;

$metric = new MCC();


  1. B. W. Matthews. (1975). Decision of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme.