Machine learningMachine learning
贝叶斯随机森林
贝叶斯随机森林通过在树结构和叶子参数上放置先验分布，然后对该集成进行后验采样或近似，来扩展经典的随机森林。其结果是一组预测，并附带校准的不确定性估计——这是标准随机森林所缺乏的能力——这使得它在了解模型有多自信与预测本身同等重要时具有价值。
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来源
- Taddy, M., Chen, C., Yu, J., & Wyle, M. (2015). Bayesian and Empirical Bayesian Forests. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), PMLR 37, 967–976. link ↗
- Lakshminarayanan, B., Roy, D. M., & Teh, Y. W. (2016). Mondrian Forests for Large-Scale Regression when Uncertainty Matters. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016), PMLR 51, 1478–1487. link ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Random Forest (Bayesian Ensemble of Decision Trees). ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-random-forest
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