Machine learningMachine learning

鲁棒随机森林

鲁棒随机森林通过引入降低异常值、标签噪声和损坏观测值影响的机制来扩展标准的随机森林集成。它不平等对待所有训练实例，而是应用加权或过滤策略，使有噪声或异常的样本对单个树分裂的贡献更小，从而在数据质量不完美时也能产生可靠的预测。

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鲁棒随机森林

Bagging（Bootstrap Aggreg…决策树梯度提升（Gradient Boosting）孤立森林 (Isolation Forest)随机森林 XGBoost 鲁棒主动学习稳健自举聚合鲁棒提升鲁棒决策树

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来源

Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18. link ↗
Random Forest. Wikipedia. link ↗

如何引用本页

ScholarGate. (2026, June 3). Robust Random Forest (Noise-Tolerant Ensemble of Decision Trees). ScholarGate. https://scholargate.app/zh/machine-learning/robust-random-forest

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.

Bagging（Bootstrap Aggregating）机器学习↔ compare
决策树机器学习↔ compare
梯度提升（Gradient Boosting）机器学习↔ compare
孤立森林 (Isolation Forest)机器学习↔ compare
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XGBoost机器学习↔ compare

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被引用于

鲁棒主动学习稳健自举聚合鲁棒提升鲁棒决策树稳健高斯过程鲁棒支持向量机鲁棒XGBoost

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