Comparer des méthodes

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