Methoden vergleichen

	Recall (Sensitivität)	Balanced Accuracy	Präzision
Fachgebiet	Modellevaluation	Modellevaluation	Modellevaluation
Familie	MCDM	MCDM	MCDM
Entstehungsjahr	20th century	2010	20th century
Urheber	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations
Typ	Evaluation metric	Evaluation metric	Evaluation metric
Wegweisende Quelle	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.
Aliasnamen	Sensitivity, True Positive Rate, TPR	Average Recall, Equal-weight Average Sensitivity	Positive Predictive Value, PPV
Verwandt	5	5	5
Zusammenfassung	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.