Comparer des méthodes

	Matrice de confusion	Exactitude
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	20th century	20th century
Auteur d'origine	Statistical foundations	Historical statistical foundations
Type	Evaluation visualization	Evaluation metric
Source fondatrice	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.
Alias	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate
Apparentées	5	5
Résumé	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.