Machine learningMachine learning
正则化提升
正则化提升通过在目标函数和更新规则中添加显式控制——收缩（学习率）、L1/L2权重惩罚、子采样和树复杂度限制——来扩展梯度提升。这些约束减少了过拟合，稳定了模型在有噪声或小数据集上的表现，也是XGBoost和LightGBM等系统在真实世界表格基准测试中持续优于普通提升的核心原因。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI: 10.1214/aos/1013203451 ↗
- Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI: 10.1145/2939672.2939785 ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized Gradient Boosting (Shrinkage and Penalized Objective Boosting). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-boosting
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Boosting机器学习↔ compare
- 梯度提升（Gradient Boosting）机器学习↔ compare
- 正则化梯度提升机器学习↔ compare
- 正则化随机森林机器学习↔ compare
- XGBoost机器学习↔ compare