Compara mètodes

	Matriu de confusió	Exactitud
Camp	Avaluació de models	Avaluació de models
Família	MCDM	MCDM
Any d'origen	20th century	20th century
Autor original	Statistical foundations	Historical statistical foundations
Tipus	Evaluation visualization	Evaluation metric
Font seminal	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.
Àlies	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate
Relacionats	5	5
Resum	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.