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Tarkkuusmatriisi×Tarkkuus×Tunnistus (herkkyys)×
TieteenalaMallien arviointiMallien arviointiMallien arviointi
MenetelmäperheMCDMMCDMMCDM
Syntyvuosi20th century20th century20th century
KehittäjäStatistical foundationsHistorical statistical foundationsHistorical statistical foundations
TyyppiEvaluation visualizationEvaluation metricEvaluation metric
AlkuperäislähdeEveritt, 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 ↗
RinnakkaisnimetError Matrix, Contingency TablePositive Predictive Value, PPVSensitivity, True Positive Rate, TPR
Liittyvät555
TiivistelmäThe 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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ScholarGateVertaile menetelmiä: Confusion Matrix · Precision · Recall (Sensitivity). Haettu 2026-06-18 osoitteesta https://scholargate.app/fi/compare