AI engineering glossary · Evaluation metrics
What is MCC (matthews correlation coefficient)?
Matthews correlation coefficient (also called MCC, phi coefficient): A single score for binary classification that uses all four confusion-matrix cells: (TP*TN - FP*FN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN)). It ranges from -1 to +1, where 0 means no better than chance. It stays informative on imbalanced data where accuracy and F1 can mislead.
Matthews correlation coefficient: an example
For TP = 15, FN = 5, FP = 10, TN = 970 the score is 14,500 / sqrt(25 * 20 * 980 * 975), about 0.66.
Related terms
More Evaluation metrics terms
- MAUVE
- Max error
- Maximum calibration error
- McNemar's test
- Mean absolute error
- Mean absolute percentage error
- Mean absolute scaled error
- Mean average precision
Where you learn it
Matthews correlation coefficient is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.