1. What is Matthews Correlation Coefficient?

The Matthews Correlation Coefficient (MCC) is a measure of the quality of binary classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure even when the classes are of very different sizes.

2. How Does the Calculator Work?

The calculator uses the MCC equation:

Where:

• \( TP \) — True Positives (correct positive predictions)
• \( TN \) — True Negatives (correct negative predictions)
• \( FP \) — False Positives (incorrect positive predictions)
• \( FN \) — False Negatives (incorrect negative predictions)

Explanation: MCC ranges from -1 (total disagreement) to +1 (perfect prediction), with 0 being no better than random.

3. Importance of MCC

Details: MCC is particularly useful when classes are imbalanced, as it provides a more informative and truthful evaluation metric than accuracy or F1 score in these cases.

4. Using the Calculator

Tips: Enter all four values (TP, TN, FP, FN) as non-negative integers. The calculator will compute MCC and display the result.

5. Frequently Asked Questions (FAQ)

Q1: What is a good MCC value?
A: Values closer to +1 indicate better performance. Generally: >0.7 excellent, 0.3-0.7 good, 0-0.3 poor, <0 indicates inverse prediction.

Q2: Why use MCC instead of accuracy?
A: MCC is more informative when classes are imbalanced, as accuracy can be misleading in these cases.

Q3: How is MCC related to chi-squared?
A: MCC is essentially the square root of the chi-squared statistic divided by the total number of observations.

Q4: Can MCC be used for multi-class problems?
A: Yes, there are multi-class generalizations of MCC available.

Q5: What does MCC=0 mean?
A: An MCC of 0 means the classifier is no better than random chance.