Сравнение методов

	Коэффициент корреляции Мэтьюса	Точность	Полнота (Чувствительность)
Область	Оценка моделей	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM	MCDM
Год появления	1975	20th century	20th century
Автор метода	Brian W. Matthews	Historical statistical foundations	Historical statistical foundations
Тип	Evaluation metric	Evaluation metric	Evaluation metric
Основополагающий источник	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.
Другие названия	Phi Coefficient, Binary Classification Correlation	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
Связанные	5	5	5
Сводка	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.