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Kļūdu matrica×Specifiskums×
NozareModeļu novērtēšanaModeļu novērtēšana
SaimeMCDMMCDM
Izcelsmes gads20th century20th century
AutorsStatistical foundationsHistorical statistical foundations
TipsEvaluation visualizationEvaluation metric
PirmavotsEveritt, 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 ↗
Citi nosaukumiError Matrix, Contingency TableTrue Negative Rate, TNR
Saistītās55
KopsavilkumsThe 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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ScholarGateSalīdzināt metodes: Confusion Matrix · Specificity. Izgūts 2026-06-17 no https://scholargate.app/lv/compare