Compară metode

	Rechemare (Sensibilitate)	Acuratețe Echilibrată	Coeficientul de Corelație Matthews	Precizie
Domeniu	Evaluarea modelelor	Evaluarea modelelor	Evaluarea modelelor	Evaluarea modelelor
Familie	MCDM	MCDM	MCDM	MCDM
Anul apariției	20th century	2010	1975	20th century
Autorul original	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	Brian W. Matthews	Historical statistical foundations
Tip	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Sursa seminală	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.	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.
Denumiri alternative	Sensitivity, True Positive Rate, TPR	Average Recall, Equal-weight Average Sensitivity	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV
Înrudite	5	5	5	5
Rezumat	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.	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.