Methoden vergleichen

	Erklärbares Stacking-Ensemble	Bagging-Ensemble
Fachgebiet	Maschinelles Lernen	Ensemble-Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1992 (stacking); 2010s–2020s (explainable extensions)	1996
Urheber	Wolpert, D. H. (stacking); XAI integration developed across the community	Leo Breiman
Typ	Ensemble meta-learning with post-hoc or intrinsic interpretability	parallel ensemble
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	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	bootstrap aggregating
Verwandt	4	4
Zusammenfassung	Explainable Stacking Ensemble combines the predictive power of stacked generalisation — training a meta-learner on the outputs of multiple diverse base models — with interpretability tools such as SHAP or LIME that reveal how each base model and each input feature contributed to the final prediction. It bridges the accuracy–transparency trade-off that makes pure stacking opaque in high-stakes settings.	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.