Machine learningMachine learning
贝叶斯 LightGBM
贝叶斯 LightGBM 将 LightGBM——一个高效的基于直方图的梯度提升框架——与贝叶斯超参数优化相结合。它不采用穷举网格搜索或随机搜索，而是利用概率代理模型来指导最优超参数的搜索，从而显著减少达到强预测性能所需的昂贵模型评估次数。
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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. In Advances in Neural Information Processing Systems, 30, 3146–3154. link ↗
- Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems, 25, 2951–2959. link ↗
如何引用本页
ScholarGate. (2026, June 3). LightGBM with Bayesian Hyperparameter Optimization. ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-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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