方法对比
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| 正则化提升× | 梯度提升（Gradient Boosting）× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2001–2016 | 2001 |
| 提出者≠ | Friedman, J. H.; extended by Chen & Guestrin | Friedman, J. H. |
| 类型≠ | Regularized ensemble (boosting with shrinkage/penalty) | Ensemble (sequential boosting of decision trees) |
| 开创性文献 | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 别名 | shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| 相关 | 5 | 5 |
| 摘要≠ | Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks. | 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. |
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