Comparer des méthodes

	Coefficient de corrélation de Matthews	Précision	Rappel (Sensibilité)
Domaine	Évaluation de modèles	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM	MCDM
Année d'origine	1975	20th century	20th century
Auteur d'origine	Brian W. Matthews	Historical statistical foundations	Historical statistical foundations
Type	Evaluation metric	Evaluation metric	Evaluation metric
Source fondatrice	Matthews, B. W. (1975). Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
Apparentées	5	5	5
Résumé	The Matthews Correlation Coefficient (MCC) is a correlation measure between predicted and actual binary classifications. It ranges from -1 to 1 and is considered one of the most reliable single-score metrics for evaluating binary classifiers, especially on imbalanced datasets.	Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.	Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.