Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| LightGBM× | Регуляризованное дерево решений× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2017 | 1984 |
| Автор метода≠ | Ke, G. et al. (Microsoft) | Breiman, L., Friedman, J., Olshen, R., & Stone, C. |
| Тип≠ | Gradient boosting decision tree ensemble | Supervised learning (regularized tree) |
| 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., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8 |
| Другие названия | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART |
| Связанные≠ | 5 | 6 |
| 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 regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees. |
| ScholarGateНабор данных ↗ |
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