Machine learningEnsemble
装袋集成
装袋（Bagging），即自助重采样集成，是一种集成学习方法，通过在训练数据的不同随机子集上训练单个学习算法的多个副本，来降低方差。每个子集通过自助采样（有放回地随机抽取样本）创建。预测通过多数投票（分类）或平均（回归）进行组合。装袋法由Leo Breiman于1996年提出，是随机森林的基础，尤其擅长降低高方差模型的过拟合。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. DOI: 10.1007/BF00058655 ↗
- Efron, B. (1979). Bootstrap methods: another look at the jackknife. The Annals of Statistics, 7(1), 1-26. DOI: 10.1214/aos/1176344552 ↗
如何引用本页
ScholarGate. (2026, June 3). Bootstrap Aggregating Ensemble. ScholarGate. https://scholargate.app/zh/ensemble-learning/bagging-ensemble
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.
Compare side by side →