Порівняння методів

	Точність	Коефіцієнт кореляції Метьюза
Галузь	Оцінювання моделей	Оцінювання моделей
Родина	MCDM	MCDM
Рік появи	20th century	1975
Автор методу	Historical statistical foundations	Brian W. Matthews
Тип	Evaluation metric	Evaluation metric
Основоположне джерело	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.
Інші назви	Overall Accuracy, Correct Classification Rate	Phi Coefficient, Binary Classification Correlation
Пов'язані	5	5
Підсумок	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.