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| Wykresy lift i gain× | Precyzja-Recall AUC× | |
|---|---|---|
| Dziedzina | Ocena modeli | Ocena modeli |
| Rodzina | MCDM | MCDM |
| Rok powstania≠ | 1990s | 2006 |
| Twórca≠ | Data mining and marketing analytics | Davis and Goadrich |
| Typ≠ | Evaluation visualization | Evaluation metric |
| Źródło pierwotne≠ | Maimon, O. Z., & Rokach, L. (Eds.). (2010). Data Mining and Knowledge Discovery Handbook (2nd ed.). Springer. DOI ↗ | Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 233-240. DOI ↗ |
| Inne nazwy | Cumulative Gain Chart, Lift Curve | PR AUC, PR Curve |
| Pokrewne≠ | 2 | 4 |
| Podsumowanie≠ | 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. | The Precision-Recall Area Under the Curve (PR AUC) is the area under the curve formed by plotting recall on the x-axis and precision on the y-axis. It is particularly useful for evaluating classifiers on imbalanced datasets, where it is often more informative than ROC AUC. |
| ScholarGateZbiór danych ↗ |
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