Porovnať metódy

	Kontingenčná tabuľka	Presnosť	Presnosť
Odbor	Hodnotenie modelov	Hodnotenie modelov	Hodnotenie modelov
Rodina	MCDM	MCDM	MCDM
Rok vzniku	20th century	20th century	20th century
Tvorca	Statistical foundations	Historical statistical foundations	Historical statistical foundations
Typ	Evaluation visualization	Evaluation metric	Evaluation metric
Pôvodný 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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Ďalšie názvy	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate	Positive Predictive Value, PPV
Príbuzné	5	5	5
Zhrnutie	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.	Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.