Сравнение методов

	Точность	Матрица ошибок
Область	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM
Год появления	20th century	20th century
Автор метода	Historical statistical foundations	Statistical foundations
Тип	Evaluation metric	Evaluation visualization
Основополагающий источник	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.
Другие названия	Overall Accuracy, Correct Classification Rate	Error Matrix, Contingency Table
Связанные	5	5
Сводка	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 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.