Сравнение методов

	Матрица ошибок	Точность
Область	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM
Год появления	20th century	20th century
Автор метода	Statistical foundations	Historical statistical foundations
Тип	Evaluation visualization	Evaluation metric
Основополагающий источник	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Другие названия	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate
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.	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.