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	Accuratezza	Accuratezza Bilanciata	Matrice di confusione	Punteggio F1	Precisione
Campo	Valutazione dei modelli	Valutazione dei modelli	Valutazione dei modelli	Valutazione dei modelli	Valutazione dei modelli
Famiglia	MCDM	MCDM	MCDM	MCDM	MCDM
Anno di origine	20th century	2010	20th century	1979	20th century
Ideatore	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	Statistical foundations	C. J. van Rijsbergen	Historical statistical foundations
Tipo	Evaluation metric	Evaluation metric	Evaluation visualization	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.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	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	Overall Accuracy, Correct Classification Rate	Average Recall, Equal-weight Average Sensitivity	Error Matrix, Contingency Table	F-measure, Harmonic Mean	Positive Predictive Value, PPV
Correlati	5	5	5	5	5
Sintesi	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	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 confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.	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.