Machine learningMachine learning
贝叶斯XGBoost
贝叶斯XGBoost 将极端梯度提升（Extreme Gradient Boosting）的预测能力与用于超参数调优的贝叶斯优化相结合。它不使用网格搜索或随机搜索，而是通过概率代理模型来指导学习率、树深度和正则化参数的最优搜索，以远少于穷举搜索的评估次数达到接近最优的性能。
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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 ↗
- Snoek, J., Larochelle, H. & Adams, R. P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25, 2951–2959. link ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian-Optimized Extreme Gradient Boosting. ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-xgboost
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