Porównaj metody

	Czułość (Recall)	Wynik F1	Precyzja
Dziedzina	Ocena modeli	Ocena modeli	Ocena modeli
Rodzina	MCDM	MCDM	MCDM
Rok powstania	20th century	1979	20th century
Twórca	Historical statistical foundations	C. J. van Rijsbergen	Historical statistical foundations
Typ	Evaluation metric	Evaluation metric	Evaluation metric
Źródło pierwotne	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Inne nazwy	Sensitivity, True Positive Rate, TPR	F-measure, Harmonic Mean	Positive Predictive Value, PPV
Pokrewne	5	5	5
Podsumowanie	Recall 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.