Confronta i metodi

	Accuratezza	Coefficiente di Correlazione di Matthews	Precisione	Richiamo (Sensibilità)
Campo	Valutazione dei modelli	Valutazione dei modelli	Valutazione dei modelli	Valutazione dei modelli
Famiglia	MCDM	MCDM	MCDM	MCDM
Anno di origine	20th century	1975	20th century	20th century
Ideatore	Historical statistical foundations	Brian W. Matthews	Historical statistical foundations	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.	Matthews, B. W. (1975). Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Alias	Overall Accuracy, Correct Classification Rate	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
Correlati	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.	The Matthews Correlation Coefficient (MCC) is a correlation measure between predicted and actual binary classifications. It ranges from -1 to 1 and is considered one of the most reliable single-score metrics for evaluating binary classifiers, especially on imbalanced datasets.	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.	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.