XStacking : An effective and inherently explainable framework for stacked ensemble learning

Abstract

Ensemble Machine Learning (EML) techniques, especially stacking, have proven effective in boosting predictive performance by combining several base models. However, traditional stacked ensembles often face challenges in predictive effectiveness of the learning space and model interpretability, which limit their practical application. In this paper, we introduce XStacking, an effective and inherently explainable framework that addresses these limitations by integrating dynamic feature transformation with model-agnostic Shapley Additive Explanations. XStacking is designed to enhance both effectiveness and transparency, ensuring high predictive accuracy and providing clear insights into model decisions. We evaluated the framework on 29 benchmark datasets for classification and regression tasks, showing its competitive performance compared to state-of-the-art stacked ensembles. Furthermore, XStacking interpretability features offer actionable insights into feature contributions and decision pathways, making it a practical and scalable solution for applications where both high performance and model transparency are critical.