Table 5.

Optimal hyper-parameters after model tuning. Summary table of the optimal hyper-parameters obtained using the strategy outlined in Section 3 and the grids defined in Table 3.

Model	Hyper-Parameters	Optimal Value	Precision	Accuracy
Decision Tree	cost_complexity tree_depth	${10}^{-10}$ 3	$0.81$	$0.82$
Radial Basis Function SVM	cost rbf_sigma	16 ${10}^{-6}$	$0.71$	$0.72$
k-NN	neighbors dist_power	3 2	$0.74$	$0.75$
Logistic Regression	threshold	$0.5$	$0.75$	$0.76$