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	Zrównoważona dokładność	Dokładność (Accuracy)	Wynik F1
Dziedzina	Ocena modeli	Ocena modeli	Ocena modeli
Rodzina	MCDM	MCDM	MCDM
Rok powstania	2010	20th century	1979
Twórca	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	C. J. van Rijsbergen
Typ	Evaluation metric	Evaluation metric	Evaluation metric
Źródło pierwotne	Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. 20th International Conference on Pattern Recognition (ICPR), 3121-3124.	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.
Inne nazwy	Average Recall, Equal-weight Average Sensitivity	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean
Pokrewne	5	5	5
Podsumowanie	Balanced accuracy is the average of recall values computed for each class separately. It corrects for class imbalance by giving equal weight to the performance on each class, regardless of class frequency in the dataset.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	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.