Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Acurácia Balanceada× | Sensibilidade× | |
|---|---|---|
| Área | Avaliação de modelos | Avaliação de modelos |
| Família | MCDM | MCDM |
| Ano de origem≠ | 2010 | 20th century |
| Autor original≠ | Brodersen, Ong, Stephan, and Buhmann | Historical statistical foundations |
| Tipo | Evaluation metric | Evaluation metric |
| Fonte 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. DOI ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Outros nomes≠ | Average Recall, Equal-weight Average Sensitivity | Sensitivity, True Positive Rate, TPR |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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