Porovnat metody

	Matice záměn	Přesnost
Obor	Hodnocení modelů	Hodnocení modelů
Rodina	MCDM	MCDM
Rok vzniku	20th century	20th century
Tvůrce	Statistical foundations	Historical statistical foundations
Typ	Evaluation visualization	Evaluation metric
Původní zdroj	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.
Další názvy	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate
Příbuzné	5	5
Shrnutí	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.