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Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.

Specificita×F1-skóre×Přesnost×
OborHodnocení modelůHodnocení modelůHodnocení modelů
RodinaMCDMMCDMMCDM
Rok vzniku20th century197920th century
TvůrceHistorical statistical foundationsC. J. van RijsbergenHistorical statistical foundations
TypEvaluation metricEvaluation metricEvaluation metric
Původní zdrojFawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann. link ↗Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗
Další názvyTrue Negative Rate, TNRF-measure, Harmonic MeanPositive Predictive Value, PPV
Příbuzné555
ShrnutíSpecificity 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.The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.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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ScholarGatePorovnat metody: Specificity · F1-Score · Precision. Získáno 2026-06-18 z https://scholargate.app/cs/compare