Salīdzināt metodes

	Kļūdu matrica	Precizitāte
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	20th century	20th century
Autors	Statistical foundations	Historical statistical foundations
Tips	Evaluation visualization	Evaluation metric
Pirmavots	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.
Citi nosaukumi	Error Matrix, Contingency Table	Positive Predictive Value, PPV
Saistītās	5	5
Kopsavilkums	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.	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.