方法证据记录
Explainable Stacking Ensemble
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.
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 Stacking Ensemble (Interpretable Meta-Learning)
分类方法记录 · ml-model / machine-learning
- Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. · DOI 10.1016/S0893-6080(05)80023-1
- Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. · URL
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