Table 8.

Final parameters of the two types of machine learning classifiers used

Classifier	Final parameters and settings
SVM	Gaussian radial basis function kernel, penalty term C = 10 (with balanced class weighting), kernel coefficient γ = number of features−1
ERT	Gini impurity as the tree-splitting metric, number of trees = 100, number of features to consider when looking for the best split Mf = number of features1/2, balanced class weighting

SVM support vector machines, ERT extremely randomised trees