Σύγκριση μεθόδων

	Σταθμισμένη Ακρίβεια	Συντελεστής Συσχέτισης Matthews	Ανάκληση (Ευαισθησία)
Πεδίο	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων
Οικογένεια	MCDM	MCDM	MCDM
Έτος προέλευσης	2010	1975	20th century
Δημιουργός	Brodersen, Ong, Stephan, and Buhmann	Brian W. Matthews	Historical statistical foundations
Τύπος	Evaluation metric	Evaluation metric	Evaluation metric
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Average Recall, Equal-weight Average Sensitivity	Phi Coefficient, Binary Classification Correlation	Sensitivity, True Positive Rate, TPR
Συναφείς	5	5	5
Σύνοψη	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.	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.