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Comparar métodos

Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.

Sensibilidade×F1-Score×Precisão×
ÁreaAvaliação de modelosAvaliação de modelosAvaliação de modelos
FamíliaMCDMMCDMMCDM
Ano de origem20th century197920th century
Autor originalHistorical statistical foundationsC. J. van RijsbergenHistorical statistical foundations
TipoEvaluation metricEvaluation metricEvaluation metric
Fonte seminalFawcett, 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 ↗
Outros nomesSensitivity, True Positive Rate, TPRF-measure, Harmonic MeanPositive Predictive Value, PPV
Relacionados555
ResumoRecall 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.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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ScholarGateComparar métodos: Recall (Sensitivity) · F1-Score · Precision. Recuperado em 2026-06-18 de https://scholargate.app/pt/compare