Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| LightGBM× | Дерево решений× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2017 | 1984 |
| Автор метода≠ | Ke, G. et al. (Microsoft) | Breiman, Friedman, Olshen & Stone |
| Тип≠ | Gradient boosting decision tree ensemble | Recursive partitioning (if-then rules) |
| Основополагающий источник≠ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154. link ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Другие названия≠ | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Связанные | 5 | 5 |
| Сводка≠ | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. | 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. |
| ScholarGateНабор данных ↗ |
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