Matthews Correlation Coefficient

A Matthews Correlation Coefficient is a binary-specific balanced correlation coefficient that can quantify classification agreement between observed classification outcomes and predicted classification outcomes for binary classification tasks.

• AKA: MCC, Matthews Correlation Coefficient, Phi Coefficient.
• Context:
  • It can typically compute Correlation Coefficient Values from confusion matrix elements through matthews correlation coefficient formulas.
  • It can typically handle Imbalanced Dataset Classification Tasks through balanced correlation measurements.
  • It can typically produce Normalized Correlation Values between negative one values and positive one values through correlation coefficient normalizations.
  • It can typically assess Binary Classification Quality through true positive rates, true negative rates, false positive rates, and false negative rates.
  • It can typically provide Single Scalar Performance Values for model comparison tasks through correlation coefficient aggregations.
  • ...
  • It can often evaluate Biological Sequence Prediction Tasks through sequence alignment correlations.
  • It can often detect Random Classification Patterns through zero correlation values.
  • It can often identify Perfect Classification Agreement through positive one correlation values.
  • It can often reveal Complete Classification Disagreement through negative one correlation values.
  • ...
  • It can range from being a Simple Matthews Correlation Coefficient Measure to being a Complex Matthews Correlation Coefficient Measure, depending on its matthews correlation coefficient computational complexity.
  • It can range from being a Binary Matthews Correlation Coefficient Measure to being a Multi-Class Matthews Correlation Coefficient Measure, depending on its matthews correlation coefficient classification scope.
  • It can range from being a Standard Matthews Correlation Coefficient Measure to being a Weighted Matthews Correlation Coefficient Measure, depending on its matthews correlation coefficient class weighting.
  • It can range from being a Single-Label Matthews Correlation Coefficient Measure to being a Multi-Label Matthews Correlation Coefficient Measure, depending on its matthews correlation coefficient label handling.
  • ...
  • It can integrate with Machine Learning Frameworks for model evaluation tasks.
  • It can complement F-Score Measures for comprehensive performance assessments.
  • It can substitute Accuracy Measures in imbalanced dataset scenarios.
  • It can combine with ROC-AUC Measures for multi-metric evaluations.
  • It can support Cross-Validation Processes through fold-wise correlation computations.
  • ...
• Example(s):
  • Matthews Correlation Coefficient Implementations, such as:
    • Scikit-Learn Matthews Correlation Coefficient Function for python-based classification evaluation.
    • R Matthews Correlation Coefficient Package for statistical classification analysis.
    • MATLAB Matthews Correlation Coefficient Implementation for numerical classification assessment.
  • Matthews Correlation Coefficient Application Domains, such as:
    • Protein Structure Prediction Matthews Correlation Coefficient for secondary structure classification.
    • Medical Diagnosis Matthews Correlation Coefficient for disease classification tasks.
    • Credit Risk Matthews Correlation Coefficient for default prediction models.
  • Matthews Correlation Coefficient Historical Developments, such as:
    • Matthews 1975 Original Formulation for biochemical sequence comparison.
    • Modern Machine Learning Matthews Correlation Coefficient for imbalanced classification problems.
  • ...
• Counter-Example(s):
  • Accuracy Measure, which lacks class imbalance handling unlike matthews correlation coefficient measures.
  • Precision Measure, which focuses on positive predictive value rather than correlation-based agreement.
  • Recall Measure, which emphasizes true positive rate without considering true negative performance.
  • F1-Score Measure, which uses harmonic mean rather than correlation coefficient.
  • Mean Squared Error, which applies to regression tasks rather than classification tasks.
• See: Correlation Coefficient, Binary Classification Performance Measure, Classification Performance Measure, Phi Coefficient, Cohen's Kappa Statistic, Confusion Matrix, Imbalanced Dataset Classification, Balanced Accuracy Measure, Chance-Corrected Measure.

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