Table 2.

Hyper-parameters search space of classifiers for optimization.

Classifiers	Hyper-parameters	Range
Extra-Trees	Estimators	600, 700, 800
	Criterion	Gini, Entropy
	Max. features	Auto, Sqrt, Log2
SVM	C	0.10 to 1.0, step = 0.10
	Kernel	Linear, Poly, rbf, Sigmoid
	Gamma	Auto, Scale
RF	Estimators	600, 700, 800
RF	Max. features	Auto, Sqrt, Log2
AdaBoost	Estimators	600, 700, 800
AdaBoost	Algorithm	SAMME, SAMME.R
MLP	Hidden layer sizes	(64), (64,64), (128), (128,128)
	Activation	identity, logistic, tanh, relu
	Solver	lbfgs, sgd, adaml
	Learning rate	constant, invscaling, adaptive
XGBoost	Estimators	600,700,800
XGBoost	Max. depth	4,5,6
GBoost	Estimators	600, 700, 800
	Criterion	friedman_mse, mse
	Max. features	auto, sqrt, log2
	Loss	deviance, exponential
LR	Penalty	l1, l2, elasticnet
LR	Solver	newton-cg, lbfgs, liblinear, sag, saga
k-NN	Number of neighbours	5 to 8, step = 1
k-NN	Algorithm	auto, ball tree, kd tree, brute
HGBoost	Max. iteration	100 to 600, step = 100
HGBoost	Loss	binary crossentropy