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| Lift- und Gain-Charts× | Recall (Sensitivität)× | |
|---|---|---|
| Fachgebiet | Modellevaluation | Modellevaluation |
| Familie | MCDM | MCDM |
| Entstehungsjahr≠ | 1990s | 20th century |
| Urheber≠ | Data mining and marketing analytics | Historical statistical foundations |
| Typ≠ | Evaluation visualization | Evaluation metric |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen≠ | Cumulative Gain Chart, Lift Curve | Sensitivity, True Positive Rate, TPR |
| Verwandt≠ | 2 | 5 |
| Zusammenfassung≠ | 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. |
| ScholarGateDatensatz ↗ |
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