Machine learningMachine learning
集成决策树
集成决策树方法训练多个决策树，并结合它们的输出来产生比任何单一决策树都更准确、更稳定的预测。它们涵盖了诸如装袋法（bagging）、随机子空间法（random subspacing）和投票法（voting）等策略，是表格数据分类和回归任务中最有效的现成技术之一。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg. DOI: 10.1007/3-540-45014-9_1 ↗
- Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. DOI: 10.1007/BF00058655 ↗
如何引用本页
ScholarGate. (2026, June 3). Ensemble Decision Tree (Combined Decision Tree Classifiers and Regressors). ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-decision-tree
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
- Boosting机器学习↔ compare
- 决策树机器学习↔ compare
- 极端随机树 (Extra Trees)机器学习↔ compare
- 随机森林机器学习↔ compare
- 投票集成 (Voting Ensemble)机器学习↔ compare