Compara mètodes

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