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	Ensemble Linear Regression	Linearna regresija (ML)
Oblast	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	1996	1805–1809
Tvorac	Breiman, L. (bagging framework)	Legendre, A.-M. & Gauss, C.F.
Tip	Ensemble of linear models	Supervised regression
Temeljni izvor	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
Drugi nazivi	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	ordinary least squares regression, OLS, least squares regression, multiple linear regression
Srodne	6	5
Sažetak	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.