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This paper aims to explore models based on the extreme gradient boosting(XGBoost) approach for business risk classification. Feature selection (FS)algorithms and hyper-parameter optimizations are simultaneously consideredduring model training. The five most commonly used FS methods including weightby Gini, weight by Chi-square, hierarchical variable clustering, weight bycorrelation, and weight by information are applied to alleviate the effect ofredundant features. Two hyper-parameter optimization approaches, random search(RS) and Bayesian tree-structured Parzen Estimator (TPE), are applied inXGBoost. The effect of different FS and hyper-parameter optimization methods onthe model performance are investigated by the Wilcoxon Signed Rank Test. Theperformance of XGBoost is compared to the traditionally utilized logisticregression (LR) model in terms of classification accuracy, area under the curve(AUC), recall, and F1 score obtained from the 10-fold cross validation. Resultsshow that hierarchical clustering is the optimal FS method for LR while weightby Chi-square achieves the best performance in XG-Boost. Both TPE and RSoptimization in XGBoost outperform LR significantly. TPE optimization shows asuperiority over RS since it results in a significantly higher accuracy and amarginally higher AUC, recall and F1 score. Furthermore, XGBoost with TPEtuning shows a lower variability than the RS method. Finally, the ranking offeature importance based on XGBoost enhances the model interpretation.Therefore, XGBoost with Bayesian TPE hyper-parameter optimization serves as anoperative while powerful approach for business risk modeling.

A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization