Statistics & Probability

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Matthews Correlation Coefficient Calculator.

Calculate this statistic instantly with validated formulas.

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Result: MCC: 0.75238122

Result

MCC: 0.75238122

Use Cases

Evaluate Binary Classification Models

Use MCC to assess the performance of machine learning models, especially when classes are imbalanced. It provides a single score that reflects the quality of classifications.

Example: Compare two classifiers on a medical diagnosis dataset to see which has a higher MCC.

Research and Academic Studies

Researchers can use MCC to report the effectiveness of their predictive models in a standardized way, making results comparable across studies.

Example: Report MCC in a paper on gene prediction algorithms.

Frequently Asked Questions

What is the Matthews Correlation Coefficient (MCC)?
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 that can be used even if the classes are of very different sizes.
How do I use this calculator?
Enter your values in the input field, separated by commas or spaces. The calculator will compute the MCC using validated formulas. Ensure you enter the correct values for true positives, true negatives, false positives, and false negatives, or the raw data depending on the calculator's design.
What does an MCC value indicate?
MCC ranges from -1 to 1. A value of 1 indicates perfect prediction, 0 indicates random prediction, and -1 indicates total disagreement between prediction and observation. It is a robust metric for binary classification performance.

Tips & Common Mistakes

Tips

  • Ensure your input values are correct: true positives, true negatives, false positives, and false negatives. Double-check the order if the calculator expects a specific sequence.
  • Use MCC when you have imbalanced classes, as it is more informative than accuracy.
  • MCC is undefined if any of the four categories sum to zero. Check your data for such cases.
  • For a quick sanity check, a perfect classifier should yield MCC = 1, and a random classifier should be near 0.

Common Mistakes to Avoid

  • Entering values in the wrong order, leading to incorrect MCC calculation.
  • Using accuracy instead of MCC for imbalanced datasets, which can be misleading.
  • Forgetting to separate values with commas or spaces, causing input errors.

Last updated: August 13, 2026