No title
This study investigates how varying hyperparameters of the Synthetic Minority Over-sampling Technique (SMOTE) affects the stability of model explanations produced by SHapley Additive exPlanations (SHAP) and Local Interpretable Modelagnostic Explanations (LIME). By using an eXtreme Gradient Boosting (XGBoost) classifier trained on 11 medical datasets with varying imbalance ratios, four SMOTE parame
