Table 2. Hyperparameter selection.

GBM	NNet	RF	SVM
Hyperparameter range per machine learning model during training
Interaction.depth =1,2,3,4,5 N.trees =1–350, by steps of 50 Shrinkage =0.1, 0.01 N.minobsinnode =1–10, by steps of 1	Size =1–10, by steps of 1 Decay =10−1−5	Mtry =1–5, by steps of 1 Splitrule = gini or splitrule Min.node.size =1–5, by steps of 1	Cost =0.1–1, by steps of 0.1 Sigma =0.1–1, by steps of 0.1
Best hyperparameter selection for testing
Interaction.depth =5 N.trees =251 Shrinkage =0.1 N.minobsinnode =1	Size =10 Decay =10−1	Mtry =1 Splitrule = gini Min.node.size =2	Cost =0.9 Sigma =1

GBM, gradient boosting machines; NNet, neural network; RF, random forest; SVM, support vector machines.