Table 4. Hyperparameter grids and selected optimal values for each machine learning model.

Model and hyperparameter		Grid search range	Optimal value
Random forest
	max_features	[’sqrt’, 'log2']	log2
	n_estimators	[50, 100, 200, 300]	50
	max_depth	[3,5,7,10]	10
	min_samples_split	[2-5]	2
AdaBoost
	n_estimators	[50, 100, 200, 300]	200
	max_depth	[3-10]	5
	learning_rate	[0.01, 0.05, 0.1, 0.5, 1.0]	1.0
Elastic Net
	alpha	[0.1, 0.5, 1.0, 2.0, 5.0]	0.1
	l1_ratio	[0.1, 0.3, 0.5, 0.7, 0.9]	0.9
Support vector regression
	C	[0.1, 1, 10, 100]	10.0
	epsilon	[0.01, 0.1, 1]	1.00
	kernel	['linear’, 'rbf’, 'poly’]	linear