Machine learningMachine learning
正则化梯度提升
正则化梯度提升在经典加性树集成（Friedman 2001）的基础上，通过将 L1 和 L2 惩罚项直接嵌入训练目标，并加入对树大小的复杂度惩罚，进行了扩展。该框架由 XGBoost（Chen & Guestrin 2016）推广，与无惩罚提升相比，它减少了过拟合，提高了泛化能力，同时保留了该方法在表格数据上的典型准确性。
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Method map
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来源
- 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 ↗
- Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI: 10.1214/aos/1013203451 ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized Gradient Boosting (L1/L2-Penalized Additive Tree Ensemble). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-gradient-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
- LightGBM机器学习↔ compare
- 正则化决策树机器学习↔ compare
- 正则化随机森林机器学习↔ compare
- XGBoost机器学习↔ compare