Linganisha mbinu

	Regressioni ya Mfumo wa Mlinganyo wa Kawaida	Regressioni ya Mtepe
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1996	1970
Mwanzilishi	Breiman, L. (bagging framework)	Hoerl, A.E. & Kennard, R.W.
Aina	Ensemble of linear models	L2-regularized linear regression
Chanzo asilia	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Majina mbadala	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Zinazohusiana	6	4
Muhtasari	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.