Сравнение методов

	Матрица ошибок	Коэффициент корреляции Мэтьюса
Область	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM
Год появления	20th century	1975
Автор метода	Statistical foundations	Brian W. Matthews
Тип	Evaluation visualization	Evaluation metric
Основополагающий источник	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	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.
Другие названия	Error Matrix, Contingency Table	Phi Coefficient, Binary Classification Correlation
Связанные	5	5
Сводка	The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.	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.