方法证据记录
Explainable Voting Ensemble
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Explainable Voting Ensemble (XAI-Augmented Voting Classifier/Regressor)
分类方法记录 · ml-model / machine-learning
- Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. · URL
- Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1–2), 1–39. · DOI 10.1007/s10462-009-9124-7
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