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Matthews Correlation Coefficient

A balanced measure for binary classification considering all confusion matrix values, robust to imbalance.

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4 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Supervised Learning ✓ understood

    Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.

  3. Classification ✓ understood

    A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.

  4. Confusion Matrix ✓ understood

    A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.

  5. Matthews Correlation Coefficient · you are here ✓ understood

A balanced measure for binary classification considering all confusion matrix values, robust to imbalance.

This concept is essential for understanding model evaluation & metrics and forms a key part of modern AI systems.

  • Confusion Matrix
  • Evaluation
  • Imbalanced Data

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Confusion Matrix
Matthews Correlation Coefficient

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Evaluation F1 Score The harmonic mean of precision and recall, providing a single metric that balances both concerns. Evaluation Cohen's Kappa A metric measuring agreement between raters/models accounting for chance agreement. Foundations Imbalanced Dataset A dataset where classes have significantly different numbers of examples, causing models to bias toward majority classes.