Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Acurácia× | Sensibilidade× | |
|---|---|---|
| Área | Avaliação de modelos | Avaliação de modelos |
| Família | MCDM | MCDM |
| Ano de origem | 20th century | 20th century |
| Autor original | Historical statistical foundations | Historical statistical foundations |
| Tipo | Evaluation metric | Evaluation metric |
| Fonte seminal | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Outros nomes≠ | Overall Accuracy, Correct Classification Rate | Sensitivity, True Positive Rate, TPR |
| Relacionados | 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. | 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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