Сравнение методов

	Матрица ошибок	Точность	Точность	Полнота (Чувствительность)
Область	Оценка моделей	Оценка моделей	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM	MCDM	MCDM
Год появления	20th century	20th century	20th century	20th century
Автор метода	Statistical foundations	Historical statistical foundations	Historical statistical foundations	Historical statistical foundations
Тип	Evaluation visualization	Evaluation metric	Evaluation metric	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.	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	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate	Positive Predictive Value, PPV	Sensitivity, True Positive Rate, TPR
Связанные	5	5	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.	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.