Compara mètodes

	Precisió equilibrada	Exactitud	Puntuació F1
Camp	Avaluació de models	Avaluació de models	Avaluació de models
Família	MCDM	MCDM	MCDM
Any d'origen	2010	20th century	1979
Autor original	Brodersen, Ong, Stephan, and Buhmann	Historical statistical foundations	C. J. van Rijsbergen
Tipus	Evaluation metric	Evaluation metric	Evaluation metric
Font seminal	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.
Àlies	Average Recall, Equal-weight Average Sensitivity	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean
Relacionats	5	5	5
Resum	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.