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We aim at developing and improving the imbalanced business risk modeling viajointly using proper evaluation criteria, resampling, cross-validation,classifier regularization, and ensembling techniques. Area Under the ReceiverOperating Characteristic Curve (AUC of ROC) is used for model comparison basedon 10-fold cross validation. Two undersampling strategies including randomundersampling (RUS) and cluster centroid undersampling (CCUS), as well as twooversampling methods including random oversampling (ROS) and Synthetic MinorityOversampling Technique (SMOTE), are applied. Three highly interpretableclassifiers, including logistic regression without regularization (LR),L1-regularized LR (L1LR), and decision tree (DT) are implemented. Twoensembling techniques, including Bagging and Boosting, are applied on the DTclassifier for further model improvement. The results show that, Boosting on DTby using the oversampled data containing 50% positives via SMOTE is the optimalmodel and it can achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and0.8907, respectively.

Predicting class-imbalanced business risk using resampling, regularization, and model ensembling algorithms