Porovnat metody

	Vyvážená přesnost	Přesnost	Citlivost (senzitivita)
Obor	Hodnocení modelů	Hodnocení modelů	Hodnocení modelů
Rodina	MCDM	MCDM	MCDM
Rok vzniku	2010	20th century	20th century
Tvůrce	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	Historical statistical foundations
Typ	Evaluation metric	Evaluation metric	Evaluation metric
Původní zdroj	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Další názvy	Average Recall, Equal-weight Average Sensitivity	Overall Accuracy, Correct Classification Rate	Sensitivity, True Positive Rate, TPR
Příbuzné	5	5	5
Shrnutí	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.	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.