Sammenlign metoder

	Robust Stacking Ensemble	Bagging (Bootstrap Aggregating)
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1992 (stacking); robust variants 2000s–present	1996
Ophavsperson	Wolpert, D. H. (stacking); robust extensions by multiple authors	Breiman, L.
Type	Ensemble (stacking with robust meta-learner)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Oprindelig kilde	Wolpert, D. H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Aliasser	robust stacking, robust stacked generalization, outlier-resistant stacking, stacking with robust meta-learner	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Relaterede	5	5
Resumé	Robust Stacking Ensemble extends classical stacked generalization by replacing the ordinary meta-learner with a robust estimator — such as a Huber-loss regressor, quantile regression, or a model trained on trimmed residuals — so that the ensemble's combination layer is resistant to outliers and noisy base-learner predictions. It improves predictive accuracy and reliability on real-world datasets with contaminated labels or heavy-tailed error distributions.	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.