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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.

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Explainable Stacking Ensemble (Interpretable Meta-Learning)

Rekod kaedah taksonomik · ml-model / machine-learning

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