Comparer des méthodes

	Rappel (Sensibilité)	Précision équilibrée	Score F1	Coefficient de corrélation de Matthews
Domaine	Évaluation de modèles	Évaluation de modèles	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM	MCDM	MCDM
Année d'origine	20th century	2010	1979	1975
Auteur d'origine	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Brian W. Matthews
Type	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Source fondatrice	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	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.	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.
Alias	Sensitivity, True Positive Rate, TPR	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	Phi Coefficient, Binary Classification Correlation
Apparentées	5	5	5	5
Résumé	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.	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.	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.