(2) Additionally, we computed permutation feature importance for both models (Figure ). This method involves shuffling the values of a single feature while keeping the others unchanged and then observing how this shuffling affects the model's performance. If the feature is important, shuffling its values causes a significant drop in model performance, as the feature's original relationship with the target has been disrupted.