Comparar métodos

	Acurácia	Acurácia Balanceada	F1-Score	Precisão
Área	Avaliação de modelos	Avaliação de modelos	Avaliação de modelos	Avaliação de modelos
Família	MCDM	MCDM	MCDM	MCDM
Ano de origem	20th century	2010	1979	20th century
Autor original	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	C. J. van Rijsbergen	Historical statistical foundations
Tipo	Evaluation metric	Evaluation metric	Evaluation metric	Evaluation metric
Fonte seminal	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	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.	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.
Outros nomes	Overall Accuracy, Correct Classification Rate	Average Recall, Equal-weight Average Sensitivity	F-measure, Harmonic Mean	Positive Predictive Value, PPV
Relacionados	5	5	5	5
Resumo	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.	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.	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.