Research on interpretative methods of machine learning models for personal credit default prediction
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.
万方数据
维普
