Table 2.

Best performing set of parameters for different classifiers.

Classifier	Best performing set of parameters
Random forest (RF)	No. of trees - 50
	Min. samples at each leaf node - 400
	Min. impurity measure at each split - $1\times {10}^{-3}$
	Max. depth of trees - 25
Neural network (NN)	No. of neurons in a hidden layer - 100
	Solver - LBFGS, Initial learning rate - $1\times {10}^{-3}$
	Max. no. of iterations - 1800
	Tolerance - $1\times {10}^{-3}$
Support vector machine (SVM)	γ - 1.0
	C-value - 1.0
	Max. no. of iterations - 5000
Adaboost classifier (AB)	No. of trees in base estimator - 2
	No. of estimators - 30
	Learning rate - 1.0
	Loss function - exponential