To boost interpretability, the SHAP framework was used to be a match-idea–primarily based approach that assigns Each individual aspect a measurable impact on predictions. K-fold cross-validation is particularly useful for stopping overfitting, as it lets us to carefully Assess a model’s predictive efficiency on unique aspects of the data