Sammenlign metoder

	Ensemble Linear Regression	Bagging (Bootstrap Aggregating)
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1996	1996
Ophavsperson	Breiman, L. (bagging framework)	Breiman, L.
Type	Ensemble of linear models	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Oprindelig kilde	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Aliasser	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Relaterede	6	5
Resumé	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.