We then plotted the Receiver Operating Characteristic (ROC) curves for the GP and ET models (Figure and Given the comparable predictive performance of GP and ET, we explored the interpretability of both models using three model interpretation methods supported in PythiaCHEM : (1) Employing SHAP we assigned importance scores to each input feature (Figure and b). The SHAP summary plot visualizes the relationship between feature values and their impact on the prediction of class 1. These scores, centered around 0, indicate the degree of association with class 1. Positive SHAP values suggest a higher association, contributing to higher predicted probabilities for this class, while negative values have a stronger association with class 0, contributing to lower predicted probabilities. The color spectrum, encoding feature values, aids in identifying the range and distribution within the dataset, with red hues representing higher feature values and blue hues representing lower feature values.