Methoden vergleichen

	Erklärbarer Voting-Ensemble	Bagging (Bootstrap Aggregating)
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2016–2020	1996
Urheber	Composite: voting ensemble (Dietterich, 2000) + XAI frameworks (Ribeiro et al., 2016; Lundberg & Lee, 2017)	Breiman, L.
Typ	Ensemble with post-hoc or ante-hoc interpretability	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Wegweisende Quelle	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Aliasnamen	XAI voting ensemble, interpretable voting classifier, transparent voting ensemble, explainable majority vote model	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Verwandt	6	5
Zusammenfassung	An Explainable Voting Ensemble combines predictions from multiple diverse base models through majority vote (hard voting) or averaged probabilities (soft voting), then applies post-hoc or ante-hoc XAI techniques — such as SHAP values, LIME, or permutation importance — to produce feature-level explanations for the combined model's decisions. The goal is to retain the accuracy gains of ensemble aggregation while meeting interpretability requirements in high-stakes or regulated applications.	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.