Table 3.

Employed models with the corresponding hyperparameters.

Classifier	Hyperparameters
LR	C: [0.1, 1.0]
	penalty: [ 'l2', None],
	solver: [ 'lbfgs', 'liblinear'],
	max_iter:[100, 200],
	class_weight:[None, 'balanced']
SVM	kernel: ['rbf','linear'],
	C': [1, 8,10]
RF	max_depth: [5, 10, 30, None],
	criterion:['gini','entropy'],
	bootstrap: [True],
	max_features:['log2', None],
	n_estimators: [50, 100, 200, 400]
XGB	learning_rate: [0.05, 0.1, 0.15],
	bytree:[.5, 1],
	max_depth: np.arange(3, 6, 10),
	n_estimators: [50, 100, 200, 400]
CB	learning_rate: [0.05, 0.1, 0.15]