Table 2. Summary of hyperparameters tuning.

Model	Hyperparameter	Range
LASSO	C (inverse of regularizer multiplier)	0.10, 0.12, 0.15, 0.18, 0.21, 0.26, 0.31, 0.37, 0.45, 0.54, 0.66, 0.79, 0.95, 1.15, 1.39, 1.68, 2.02, 2.44, 2.95, 3.56, 4.29, 5.18, 6.25, 7.54, 9.10,10.9, 13.3, 16.0, 19.3, 23.3, 28.1, 33.9, 40.9, 49.4, 59.6, 72.0, 86.9, 105, 126, 153, 184, 222, 268, 324, 391, 471, 569, 687, 829, 1000
Elastic net	L1 ratio	0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95
	Alpha	0.00001, 0.00004, 0.00016, 0.0006, 0.0025, 0.01, 0.04, 0.16, 0.63, 2.5, 10
CatBoost	Tree depth	2, 4
	Learning rate	0.03, 0.1, 0.3
	Bagging temperature	0.6, 0.8, 1.
	L2 leaf regularization	3, 10, 100, 500
	Leaf estimation iterations	1, 2
MLP	Number of hidden neurons	5, 10, 15, 20
	Learning rate	0.001, 0.01
	Batch size	16, 32
	Dropout rate	0.1, 0.2
	L1 regularization ratio	0.0001, 0.001

The table details the hyperparameters and corresponding range that were tuned for each model in the cross-validation process.