Table 2.

The Five model parameter settings

Model	Parameter setting
Logistic Regression Model	Parameters were set to default values
Decision Tree	The node splitting criterion was Gini diversity index, node partition mode was best, the maximum number of splits was 100, the maximum depth of the tree was unlimited, and other parameters were set to default values
Naïve Baye	The function was Gaussian radial kernel function (RBF), the smoothing (alpha value) was 1, and other parameters were set to default values
Support Vector Machine	The penalty coefficient of error term was 1, the kernel was RBF, the kernel coefficient value was 0.01, the multiclassification decision function was over, the model convergence parameter was 0.001, the maximum number of iterations was 2000, and other parameters were set to default values
AdaBoost	The learner type was the decision tree, the maximum number of splits was 20, the number of learners was 30, and the learning rate was 0.1