In recent years,as the demand for personal credit business has surged,financial institutions predict credit default with machine learning models.The interpretability of the prediction results is so important that influences the decision-making of financial institutions.Firstly,a personal credit default prediction model is constructed based on machine learning models Light-GBM,XGBoost and CatBoost.Then,the model is experimentally optimized by using hyperparameter optimization algorithms and Voting fusion methods.Finally,the model prediction results are interpretively analyzed globally and locally through four interpreta-tive methods,including Permutation Feature Importance,LIME,SHAP and Counterfactual Interpretation,which greatly enhance the reliability and practicability of the model.
维普
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