Below is an example of how one might interpret and discuss the feature-importance results for the three models (Random Forest, Gradient Boost, and XGBoost) in the context of predicting the PFAS distribution coefficient (Kd) and adsorption behavior. The values shown are the raw "importance" outputs from each algorithm rather than normalized scores, so direct comparisons within a single model are typically more meaningful than across models. Nevertheless, looking at all three can provide insights into which variables tend to be more (or less) influential overall.