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F1-Score×Präzision×Recall (Sensitivität)×
FachgebietModellevaluationModellevaluationModellevaluation
FamilieMCDMMCDMMCDM
Entstehungsjahr197920th century20th century
UrheberC. J. van RijsbergenHistorical statistical foundationsHistorical statistical foundations
TypEvaluation metricEvaluation metricEvaluation metric
Wegweisende Quellevan 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 ↗Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗
AliasnamenF-measure, Harmonic MeanPositive Predictive Value, PPVSensitivity, True Positive Rate, TPR
Verwandt555
ZusammenfassungThe 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.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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ScholarGateMethoden vergleichen: F1-Score · Precision · Recall (Sensitivity). Abgerufen am 2026-06-18 von https://scholargate.app/de/compare