Machine learning
Bagging（Bootstrap Aggregating）
Bagging，是“Bootstrap Aggregating”的简称，由Leo Breiman于1996年提出的一种集成元算法。它在从训练数据中独立抽取的自助样本上训练基学习器的多个副本，并通过平均（用于回归）或多数投票（用于分类）来组合它们的预测，从而生成一个方差比任何单个基学习器显著低的最终预测器。
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来源
- Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI: 10.1007/BF00058655 ↗
- Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 8.7). Springer. ISBN: 978-0-387-84857-0
- James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 8.2). Springer. ISBN: 978-1-4614-7138-7
如何引用本页
ScholarGate. (2026, June 3). Bagging (Bootstrap Aggregating). ScholarGate. https://scholargate.app/zh/machine-learning/bagging
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.
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