Machine learningMachine learning
极端随机树 (Extra Trees)
极端随机树（Extra Trees），由 Geurts、Ernst 和 Wehenkel 于 2006 年提出，是一种决策树集成方法，其随机化程度比随机森林 (Random Forest) 更高。在每个节点，候选特征和分裂阈值都完全随机选择，消除了对阈值的贪婪搜索。这种额外的随机性降低了方差，通常能达到或超过随机森林的准确率，并且在训练时运行速度显著更快。
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来源
- Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI: 10.1007/s10994-006-6226-1 ↗
- Extra-Trees. Wikipedia. link ↗
如何引用本页
ScholarGate. (2026, June 3). Extremely Randomized Trees (Extra-Trees). ScholarGate. https://scholargate.app/zh/machine-learning/extra-trees
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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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