Módszerek összehasonlítása

	Szenzitivitás (Recall)	Kiegyensúlyozott pontosság	Precízió
Tudományterület	Modellértékelés	Modellértékelés	Modellértékelés
Módszercsalád	MCDM	MCDM	MCDM
Keletkezés éve	20th century	2010	20th century
Megalkotó	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations
Típus	Evaluation metric	Evaluation metric	Evaluation metric
Alapmű	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.
Alternatív nevek	Sensitivity, True Positive Rate, TPR	Average Recall, Equal-weight Average Sensitivity	Positive Predictive Value, PPV
Kapcsolódó	5	5	5
Összefoglaló	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.