(3) As ET is a decision-tree-based model, we calculated the Gini importance for the features (Figure ). Gini importance measures the reduction in Gini impurity achieved by each feature across all decision tree nodes in the ensemble. Higher Gini importance indicates that a feature is more critical in splitting the data effectively during the tree-building process, leading to improved overall performance of the decision tree ensemble. It helps to identify the most important features of the model's global performance.