Comparer des méthodes

	Coefficient de corrélation de Matthews	Précision équilibrée	Score F1
Domaine	Évaluation de modèles	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM	MCDM
Année d'origine	1975	2010	1979
Auteur d'origine	Brian W. Matthews	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen
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.	Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. 20th International Conference on Pattern Recognition (ICPR), 3121-3124.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Alias	Phi Coefficient, Binary Classification Correlation	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean
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.	Balanced accuracy is the average of recall values computed for each class separately. It corrects for class imbalance by giving equal weight to the performance on each class, regardless of class frequency in the dataset.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.