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Матрица ошибок×F1-мера×Точность×
ОбластьОценка моделейОценка моделейОценка моделей
СемействоMCDMMCDMMCDM
Год появления20th century197920th century
Автор методаStatistical foundationsC. J. van RijsbergenHistorical statistical foundations
ТипEvaluation visualizationEvaluation metricEvaluation metric
Основополагающий источникEveritt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC. link ↗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 названияError Matrix, Contingency TableF-measure, Harmonic MeanPositive Predictive Value, PPV
Связанные555
СводкаThe confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.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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ScholarGateСравнение методов: Confusion Matrix · F1-Score · Precision. Получено 2026-06-18 из https://scholargate.app/ru/compare