Methoden vergleichen

	Ensemble-Lineare Regression	Lineare Regression (ML)
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1996	1805–1809
Urheber	Breiman, L. (bagging framework)	Legendre, A.-M. & Gauss, C.F.
Typ	Ensemble of linear models	Supervised regression
Wegweisende Quelle	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7
Aliasnamen	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	ordinary least squares regression, OLS, least squares regression, multiple linear regression
Verwandt	6	5
Zusammenfassung	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.	Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task.