Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Дерево решений× | LIME: Локально-интерпретируемые модельно-агностические объяснения× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1984 | 2016 |
| Автор метода≠ | Breiman, Friedman, Olshen & Stone | Marco Ribeiro, Sameer Singh & Carlos Guestrin |
| Тип≠ | Recursive partitioning (if-then rules) | post-hoc local explanation |
| Основополагающий источник≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144. DOI ↗ |
| Другие названия≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar |
| Связанные≠ | 5 | 2 |
| Сводка≠ | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors. |
| ScholarGateНабор данных ↗ |
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