Combining Weak Learner Explanations to Enhance Random Forest Explainability and Robustness

The author argues that combining explanations from weak learners can enhance the robustness of ensemble model explanations, leading to more trustworthy results. By selecting only positive-contributing weak learners, the proposed method significantly improves explanation robustness compared to direct XAI application.