Machine learningMachine learning
集成线性回归
集成线性回归（Ensemble Linear Regression）结合了多个普通最小二乘（ordinary least-squares, OLS）模型——每个模型都在不同的自举样本（bootstrap sample）或特征子集上拟合——并对它们的预测进行平均。该技术植根于Breiman（1996）的bagging框架，与单一的线性回归拟合相比，它能降低方差并提高预测稳定性，同时保留线性假设的可解释性。
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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). Springer. ISBN: 978-0-387-84857-0
如何引用本页
ScholarGate. (2026, June 3). Ensemble of Linear Regression Models (Bagged and Stacked Linear Regression). ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-linear-regression
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
- 线性回归 (ML)机器学习↔ compare
- 随机森林机器学习↔ compare
- 正则化线性回归机器学习↔ compare
- 岭回归（Ridge Regression）机器学习↔ compare
- 投票集成 (Voting Ensemble)机器学习↔ compare