Confronta i metodi

	Accuratezza Bilanciata	Precisione	Richiamo (Sensibilità)
Campo	Valutazione dei modelli	Valutazione dei modelli	Valutazione dei modelli
Famiglia	MCDM	MCDM	MCDM
Anno di origine	2010	20th century	20th century
Ideatore	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	Historical statistical foundations
Tipo	Evaluation metric	Evaluation metric	Evaluation metric
Fonte seminale	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.
Alias	Average Recall, Equal-weight Average Sensitivity	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
Correlati	5	5	5
Sintesi	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.	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.