Meta‐Learning Credit Risk Prediction by Fusing Regularized Logistic Regression and Random Forest

Abstract

Credit risk assessment is of vital importance as it can predict whether an individual will default on a loan. Classifying loan applicants into “good” or “bad” debtors can help lending institutions make informed decisions. One challenge in credit default prediction is how to avoid the interference of redundant and irrelevant features, which may disrupt the classification results. This paper uses a meta‐learning approach for credit risk prediction based on /‐regularized logistic regression (RLR) and random forest (RF) to address this issue. The model reduces overfitting through RLR's weight constraint mechanism and models nonlinear feature dependencies using RF's ensemble of decision trees. A dynamic weight allocation mechanism in the meta‐learning layer adaptively adjusts the predictive contributions of RLR and RF based on dataset characteristics, simultaneously optimizing feature weighting and model fusion strategies. We conducted an analysis of the German Credit dataset and the Lending Club dataset from three aspects: classification results, methodological differences, and interpretability. The experimental results demonstrate that elastic net–RF meta‐learning (Enet–RF Meta) outperforms single models in accuracy, AUC, and F1 score. Feature importance analysis identifies key variables influencing predictions, while violin plots reveal varying reliance on linear features across datasets. The approach offers enhanced predictive performance and interpretability, making it a robust solution for credit risk assessment.