방법 비교

	앙상블 선형 회귀	배깅 (Bootstrap Aggregating)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1996	1996
창시자	Breiman, L. (bagging framework)	Breiman, L.
유형	Ensemble of linear models	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
원전	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
별칭	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
관련	6	5
요약	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.