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Kontingenčná tabuľka×Presnosť×Citlivosť (Recall)×
OdborHodnotenie modelovHodnotenie modelovHodnotenie modelov
RodinaMCDMMCDMMCDM
Rok vzniku20th century20th century20th century
TvorcaStatistical foundationsHistorical statistical foundationsHistorical statistical foundations
TypEvaluation visualizationEvaluation metricEvaluation metric
Pôvodný zdrojEveritt, 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 ↗Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗
Ďalšie názvyError Matrix, Contingency TablePositive Predictive Value, PPVSensitivity, True Positive Rate, TPR
Príbuzné555
ZhrnutieThe 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.Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.
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ScholarGatePorovnať metódy: Confusion Matrix · Precision · Recall (Sensitivity). Získané 2026-06-19 z https://scholargate.app/sk/compare