Methoden vergleichen

	Robustes Stacking-Ensemble	Bagging (Bootstrap Aggregating)
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1992 (stacking); robust variants 2000s–present	1996
Urheber	Wolpert, D. H. (stacking); robust extensions by multiple authors	Breiman, L.
Typ	Ensemble (stacking with robust meta-learner)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Wegweisende Quelle	Wolpert, D. H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Aliasnamen	robust stacking, robust stacked generalization, outlier-resistant stacking, stacking with robust meta-learner	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Verwandt	5	5
Zusammenfassung	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.