Comparer des méthodes

	Précision équilibrée	Exactitude	Score F1	Rappel (Sensibilité)
Domaine	Évaluation de modèles	Évaluation de modèles	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM	MCDM	MCDM
Année d'origine	2010	20th century	1979	20th century
Auteur d'origine	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	C. J. van Rijsbergen	Historical statistical foundations
Type	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Source fondatrice	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	Average Recall, Equal-weight Average Sensitivity	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean	Sensitivity, True Positive Rate, TPR
Apparentées	5	5	5	5
Résumé	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.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	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.	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.