Table 2.

Definition and implementation of the ten models in this study.

Classifier	Implementation in Python 3.6
NB	sklearn.naive_bayes. GaussianNB()
LR	sklearn.linear_model.logisticRegressionCV(max_iter = 100,000, solver = “liblinear”)
DT	sklearn.tree. DecisionTreeClassifier()
GBDT	sklearn.ensemble.GradientBoostingClassifier()
nn	sklearn.neural_network. MLPClassifier (hidden_layer_sizes = (400, 100), alpha = 0.01, max_iter = 10,000)
KNN	sklearn.neighbors. sklearn.neighbors()
Ada	sklearn.ensemble.AdaBoostClassifier()
DA	sklearn.discriminant_analysis()
RF	sklearn.ensemble.RandomForestClassifier(n_estimators = 200)
SVM	sklearn.svm.SVC(kernel = ‘rbf’,probability = True)