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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- 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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