Addressing class imbalance in probability-of-default modeling: a comparative study of SMOTE, ensemble learning and explainable artificial intelligence
The author puts forward s a governance-aligned framework for probability of default modeling under class imbalance, combining domain-informed feature engineering, resampling, ensemble learning and explainable artificial intelligence
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