Comparar métodos

	Exactitud	Matriz de confusión
Campo	Evaluación de modelos	Evaluación de modelos
Familia	MCDM	MCDM
Año de origen	20th century	20th century
Autor original	Historical statistical foundations	Statistical foundations
Tipo	Evaluation metric	Evaluation visualization
Fuente 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.
Alias	Overall Accuracy, Correct Classification Rate	Error Matrix, Contingency Table
Relacionados	5	5
Resumen	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.