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Recall (Sensitivitas)×Skor-F1×Presisi×
BidangEvaluasi ModelEvaluasi ModelEvaluasi Model
KeluargaMCDMMCDMMCDM
Tahun asal20th century197920th century
PencetusHistorical statistical foundationsC. J. van RijsbergenHistorical statistical foundations
TipeEvaluation metricEvaluation metricEvaluation metric
Sumber perintisFawcett, 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 ↗
AliasSensitivity, True Positive Rate, TPRF-measure, Harmonic MeanPositive Predictive Value, PPV
Terkait555
RingkasanRecall 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.
ScholarGateSet data
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ScholarGateBandingkan metode: Recall (Sensitivity) · F1-Score · Precision. Diakses 2026-06-18 dari https://scholargate.app/id/compare