Methoden vergleichen

	Erklärbares Stacking-Ensemble	Bagging-Ensemble	Random Forest
Fachgebiet	Maschinelles Lernen	Ensemble-Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning	Machine learning
Entstehungsjahr	1992 (stacking); 2010s–2020s (explainable extensions)	1996	2001
Urheber	Wolpert, D. H. (stacking); XAI integration developed across the community	Leo Breiman	Breiman, L.
Typ	Ensemble meta-learning with post-hoc or intrinsic interpretability	parallel ensemble	Ensemble (bagging of decision trees)
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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliasnamen	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	bootstrap aggregating	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwandt	4	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.