مقایسهٔ روش‌ها

	دقت موزون	دقت	ضریب همبستگی متیوز (MCC)	بازیابی (حساسیت)
حوزه	ارزیابی مدل	ارزیابی مدل	ارزیابی مدل	ارزیابی مدل
خانواده	MCDM	MCDM	MCDM	MCDM
سال پیدایش	2010	20th century	1975	20th century
پدیدآور	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	Brian W. Matthews	Historical statistical foundations
نوع	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
منبع بنیادین	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.	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.
نام‌های دیگر	Average Recall, Equal-weight Average Sensitivity	Overall Accuracy, Correct Classification Rate	Phi Coefficient, Binary Classification Correlation	Sensitivity, True Positive Rate, TPR
مرتبط	5	5	5	5
خلاصه	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.	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.	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.