Machine learningMachine learning
正则化 LightGBM
正则化 LightGBM 在 LightGBM（微软高效梯度提升框架）的叶节点权重目标函数中应用 L1（套索）和 L2（岭回归）惩罚项，以控制模型复杂度、减少过拟合，并提高在具有高维或噪声特征集的表格分类和回归任务上的泛化能力。
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来源
- Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. link ↗
- 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 Light Gradient Boosting Machine. ScholarGate. https://scholargate.app/zh/machine-learning/regularized-lightgbm
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.
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- 梯度提升（Gradient Boosting）机器学习↔ compare
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- 正则化梯度提升机器学习↔ compare
- XGBoost机器学习↔ compare