手法を比較

	説明可能な投票アンサンブル	スタッキング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2016–2020	1992
提唱者	Composite: voting ensemble (Dietterich, 2000) + XAI frameworks (Ribeiro et al., 2016; Lundberg & Lee, 2017)	Wolpert, D.H.
種類	Ensemble with post-hoc or ante-hoc interpretability	Ensemble (heterogeneous meta-learning)
原典	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
別名	XAI voting ensemble, interpretable voting classifier, transparent voting ensemble, explainable majority vote model	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
関連	6	5
概要	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.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.