Compara mètodes

	Recordació (Sensibilitat)	Precisió equilibrada	Precisió
Camp	Avaluació de models	Avaluació de models	Avaluació de models
Família	MCDM	MCDM	MCDM
Any d'origen	20th century	2010	20th century
Autor original	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations
Tipus	Evaluation metric	Evaluation metric	Evaluation metric
Font seminal	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Àlies	Sensitivity, True Positive Rate, TPR	Average Recall, Equal-weight Average Sensitivity	Positive Predictive Value, PPV
Relacionats	5	5	5
Resum	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.	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.