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Forvirringsmatrix×Specificitet×
FagområdeModelevalueringModelevaluering
FamilieMCDMMCDM
Oprindelsesår20th century20th century
OphavspersonStatistical foundationsHistorical statistical foundations
TypeEvaluation visualizationEvaluation metric
Oprindelig kildeEveritt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC. link ↗Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗
AliasserError Matrix, Contingency TableTrue Negative Rate, TNR
Relaterede55
ResuméThe 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.Specificity measures the proportion of actual negative cases that were correctly identified as negative by the classifier. It answers the question: 'Of all the cases that were truly negative, how many did we correctly reject?' Specificity is complementary to recall and is essential when false positives are costly.
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ScholarGateSammenlign metoder: Confusion Matrix · Specificity. Hentet 2026-06-17 fra https://scholargate.app/da/compare