Bandingkan metode

	Regresi Linear Ensemble	Regresi Linear Terregularisasi
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1996	1970–2005
Pencetus	Breiman, L. (bagging framework)	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
Tipe	Ensemble of linear models	Penalized linear model
Sumber perintis	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
Terkait	6	4
Ringkasan	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.	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.