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Matrica zabune×Specifičnost×
PodručjeEvaluacija modelaEvaluacija modela
ObiteljMCDMMCDM
Godina nastanka20th century20th century
TvoracStatistical foundationsHistorical statistical foundations
VrstaEvaluation visualizationEvaluation metric
Temeljni izvorEveritt, 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 ↗
Drugi naziviError Matrix, Contingency TableTrue Negative Rate, TNR
Srodne55
SažetakThe 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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ScholarGateUsporedite metode: Confusion Matrix · Specificity. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare