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| 그래디언트 부스팅× | 정규화된 결정 트리× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2001 | 1984 |
| 창시자≠ | Friedman, J. H. | Breiman, L., Friedman, J., Olshen, R., & Stone, C. |
| 유형≠ | Ensemble (sequential boosting of decision trees) | Supervised learning (regularized tree) |
| 원전≠ | 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 |
| 별칭 | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART |
| 관련≠ | 5 | 6 |
| 요약≠ | 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. |
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