The Matthews correlation coefficient takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient between the observed and predicted binary classifications; it returns a value between ?1 and +1. A coefficient of +1 represents a perfect prediction, 0 no better than random prediction and ?1 indicates total disagreement between prediction and observation. The statistic is also known as the phi coefficient. MCC is related to the chi-square statistic for a 2×2 contingency table. The MCC can be calculated directly from the confusion matrix using the formula: $$ \text{MCC} = \frac{ TP \times TN - FP \times FN } {\sqrt{ (TP + FP) ( TP + FN ) ( TN + FP ) ( TN + FN ) } } $$ See: http://en.wikipedia.org/wiki/Matthews_correlation_coefficient
| Minimum value | -1 |
| Maximum value | 1 |
| Unit | |
| Optimization | Higher is better |