Machine learning
LightGBM
LightGBM 是微软推出的梯度提升决策树实现，由 Ke 等人于 2017 年提出，它采用叶子生长（leaf-wise）方式构建树，并将特征分箱为直方图以提高速度。在大型数据集上，它比 XGBoost 快得多，同时保持了强大的预测精度。
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Method map
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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 (NeurIPS) 30, 3146–3154. link ↗
如何引用本页
ScholarGate. (2026, June 1). Light Gradient Boosting Machine. ScholarGate. https://scholargate.app/zh/machine-learning/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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- XGBoost机器学习↔ compare