Comparar métodos

	Acurácia	Acurácia Balanceada	Matriz de Confusão	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	20th century	20th century
Autor original	Historical statistical foundations	Brodersen, Ong, Stephan, and Buhmann	Statistical foundations	Historical statistical foundations
Tipo	Evaluation metric	Evaluation metric	Evaluation visualization	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.	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	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	Error Matrix, Contingency Table	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 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.	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.