Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Precision-Recall AUC× | Полнота (Чувствительность)× | |
|---|---|---|
| Область | Оценка моделей | Оценка моделей |
| Семейство | MCDM | MCDM |
| Год появления≠ | 2006 | 20th century |
| Автор метода≠ | Davis and Goadrich | Historical statistical foundations |
| Тип | Evaluation metric | Evaluation metric |
| Основополагающий источник≠ | 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 ↗ | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. DOI ↗ |
| Другие названия≠ | PR AUC, PR Curve | Sensitivity, True Positive Rate, TPR |
| Связанные≠ | 4 | 5 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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