Table 2. Values of parameters.

Method	Parameter
SVM	kernel: ‘RBF’; regularization: 1.0; kernel function degree: 3
SGD	loss: ‘hinge’; penalty: l2; regularization (α): 0.0001
NC	distance metric: ‘euclidean’
DT	split criterion: ‘gini’; split strategy: ‘best’; maximum tree depth (max_depth): ‘None’
NB	largest feature variance: 10−9
Extra trees	number of trees: 100; split criterion: ‘gini’; maximum tree depth (max_depth): ‘None’
Regression	fit_intercept: ‘True’; normalize feature (normalize): ‘False’
KBinsDiscretizer	’Number of bins’: 5