  The objective of this study is to develop a good risk model for classifying
business delinquency by simultaneously exploring several machine learning based
methods including regularization, hyper-parameter optimization, and model
ensembling algorithms. The rationale under the analyses is firstly to obtain
good base binary classifiers (include Logistic Regression ($LR$), K-Nearest
Neighbors ($KNN$), Decision Tree ($DT$), and Artificial Neural Networks
($ANN$)) via regularization and appropriate settings of hyper-parameters. Then
two model ensembling algorithms including bagging and boosting are performed on
the good base classifiers for further model improvement. The models are
evaluated using accuracy, Area Under the Receiver Operating Characteristic
Curve (AUC of ROC), recall, and F1 score via repeating 10-fold cross-validation
10 times. The results show the optimal base classifiers along with the
hyper-parameter settings are $LR$ without regularization, $KNN$ by using 9
nearest neighbors, $DT$ by setting the maximum level of the tree to be 7, and
$ANN$ with three hidden layers. Bagging on $KNN$ with $K$ valued 9 is the
optimal model we can get for risk classification as it reaches the average
accuracy, AUC, recall, and F1 score valued 0.90, 0.93, 0.82, and 0.89,
respectively.
