  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 considered
during model training. The five most commonly used FS methods including weight
by Gini, weight by Chi-square, hierarchical variable clustering, weight by
correlation, and weight by information are applied to alleviate the effect of
redundant features. Two hyper-parameter optimization approaches, random search
(RS) and Bayesian tree-structured Parzen Estimator (TPE), are applied in
XGBoost. The effect of different FS and hyper-parameter optimization methods on
the model performance are investigated by the Wilcoxon Signed Rank Test. The
performance of XGBoost is compared to the traditionally utilized logistic
regression (LR) model in terms of classification accuracy, area under the curve
(AUC), recall, and F1 score obtained from the 10-fold cross validation. Results
show that hierarchical clustering is the optimal FS method for LR while weight
by Chi-square achieves the best performance in XG-Boost. Both TPE and RS
optimization in XGBoost outperform LR significantly. TPE optimization shows a
superiority over RS since it results in a significantly higher accuracy and a
marginally higher AUC, recall and F1 score. Furthermore, XGBoost with TPE
tuning shows a lower variability than the RS method. Finally, the ranking of
feature importance based on XGBoost enhances the model interpretation.
Therefore, XGBoost with Bayesian TPE hyper-parameter optimization serves as an
operative while powerful approach for business risk modeling.
