Table 3.

Description of best-selected parameters of machine learning models.

Algorithm	Parameters
LR	penalty: l2, solver: lbfgs, max_iter: 100
KNN	n_neighbors: 8, algoritgm: kd_tree, p: 2
DT	criterion: gini, spliter: best, max_depth: none, max_features: none
SVM	C: 5, gamma: 1, kernel: rbf, decision_function_shape: ovr
NB	var_smoothing: 1e-1
ET	n_estimators: 100, criterion: gini, max_depth: none, max_features: none
RF	n_estimators: 100, criterion: gini, max_depth: none, max_features: none
XGBOOST	booster: gbtree, eta: 0.3, max_depth: 6, sampling method: gradiant_based