Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Gráfico de Lift e Gain× | Sensibilidade× | |
|---|---|---|
| Área | Avaliação de modelos | Avaliação de modelos |
| Família | MCDM | MCDM |
| Ano de origem≠ | 1990s | 20th century |
| Autor original≠ | Data mining and marketing analytics | Historical statistical foundations |
| Tipo≠ | Evaluation visualization | Evaluation metric |
| Fonte seminal≠ | Maimon, O. Z., & Rokach, L. (Eds.). (2010). Data Mining and Knowledge Discovery Handbook (2nd ed.). Springer. DOI ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Outros nomes≠ | Cumulative Gain Chart, Lift Curve | Sensitivity, True Positive Rate, TPR |
| Relacionados≠ | 2 | 5 |
| Resumo≠ | Lift and gain charts visualize classifier performance by showing how much better the model performs compared to random selection, particularly useful for ranking or scoring tasks where you select a top percentage of samples. They are widely used in marketing, credit scoring, and fraud detection. | 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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