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Ensemble Linear Regression

Ensemble Linear Regression combines multiple ordinary least-squares models — each fitted on a different bootstrap sample or feature subset — and averages their predictions. The technique, grounded in Breiman's bagging framework (1996), reduces variance and improves predictive stability compared with a single linear regression fit, while retaining the interpretability of linear assumptions.

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Ensemble of Linear Regression Models (Bagged and Stacked Linear Regression)

Taxonomisk metodpost · ml-model / machine-learning

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

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