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Полнота (Чувствительность)×Точность×
ОбластьОценка моделейОценка моделей
СемействоMCDMMCDM
Год появления20th century20th century
Автор методаHistorical statistical foundationsHistorical statistical foundations
ТипEvaluation metricEvaluation metric
Основополагающий источникFawcett, 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 ↗
Другие названияSensitivity, True Positive Rate, TPRPositive Predictive Value, PPV
Связанные55
СводкаRecall 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.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.
ScholarGateНабор данных
  1. v1
  2. 2 Источники
  3. PUBLISHED
  1. v1
  2. 2 Источники
  3. PUBLISHED

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ScholarGateСравнение методов: Recall (Sensitivity) · Precision. Получено 2026-06-17 из https://scholargate.app/ru/compare