Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Градиентный бустинг× | Регуляризованное дерево решений× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2001 | 1984 |
| Автор метода≠ | Friedman, J. H. | Breiman, L., Friedman, J., Olshen, R., & Stone, C. |
| Тип≠ | Ensemble (sequential boosting of decision trees) | Supervised learning (regularized tree) |
| Основополагающий источник≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8 |
| Другие названия | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART |
| Связанные≠ | 5 | 6 |
| Сводка≠ | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | 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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