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Macierz pomyłek×Specyficzność×
DziedzinaOcena modeliOcena modeli
RodzinaMCDMMCDM
Rok powstania20th century20th century
TwórcaStatistical foundationsHistorical statistical foundations
TypEvaluation visualizationEvaluation metric
Źródło pierwotneEveritt, 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 ↗
Inne nazwyError Matrix, Contingency TableTrue Negative Rate, TNR
Pokrewne55
PodsumowanieThe 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.
ScholarGateZbiór danych
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ScholarGatePorównaj metody: Confusion Matrix · Specificity. Pobrano 2026-06-17 z https://scholargate.app/pl/compare