Confronta i metodi

	Specificità	Accuratezza Bilanciata	Punteggio F1	Precisione
Campo	Valutazione dei modelli	Valutazione dei modelli	Valutazione dei modelli	Valutazione dei modelli
Famiglia	MCDM	MCDM	MCDM	MCDM
Anno di origine	20th century	2010	1979	20th century
Ideatore	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Historical statistical foundations
Tipo	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Fonte seminale	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.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	True Negative Rate, TNR	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	Positive Predictive Value, PPV
Correlati	5	5	5	5
Sintesi	Specificity measures the proportion of actual negative cases that were correctly identified as negative by the classifier. It answers the question: 'Of all the cases that were truly negative, how many did we correctly reject?' Specificity is complementary to recall and is essential when false positives are 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.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.	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.