Gradient boosting models with SMOTE oversampling show better minority-class sensitivity than statistical baselines for financial distress prediction under severe imbalance.
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Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints
Gradient boosting models with SMOTE oversampling show better minority-class sensitivity than statistical baselines for financial distress prediction under severe imbalance.