ScholarGate
Ассистент

Сравнение методов

Просматривайте выбранные методы рядом; строки с различиями подсвечены.

Полнота (Чувствительность)×F1-мера×Точность×
ОбластьОценка моделейОценка моделейОценка моделей
СемействоMCDMMCDMMCDM
Год появления20th century197920th century
Автор методаHistorical statistical foundationsC. J. van RijsbergenHistorical statistical foundations
ТипEvaluation metricEvaluation metricEvaluation metric
Основополагающий источникFawcett, 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 ↗
Другие названияSensitivity, True Positive Rate, TPRF-measure, Harmonic MeanPositive Predictive Value, PPV
Связанные555
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.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.
ScholarGateНабор данных
  1. v1
  2. 2 Источники
  3. PUBLISHED
  1. v1
  2. 2 Источники
  3. PUBLISHED
  1. v1
  2. 2 Источники
  3. PUBLISHED

Перейти к поиску Скачать слайды

ScholarGateСравнение методов: Recall (Sensitivity) · F1-Score · Precision. Получено 2026-06-18 из https://scholargate.app/ru/compare