Decision-Aware and Explainable Machine Learning for Credit Risk Prediction: Reproducible Experiments

Code and PAPER-mode results for two credit risk datasets, including class weighting, probability calibration, asymmetric threshold costs, probability-scale SHAP–LIME comparison, and independent-neighborhood LIME fidelity analyses.