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Spécificité×Précision×
DomaineÉvaluation de modèlesÉvaluation de modèles
FamilleMCDMMCDM
Année d'origine20th century20th century
Auteur d'origineHistorical statistical foundationsHistorical statistical foundations
TypeEvaluation metricEvaluation metric
Source fondatriceFawcett, 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 ↗
AliasTrue Negative Rate, TNRPositive Predictive Value, PPV
Apparentées55
RésuméSpecificity 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.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.
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ScholarGateComparer des méthodes: Specificity · Precision. Consulté le 2026-06-15 sur https://scholargate.app/fr/compare