Table A3.

Hyperparameters selected for the best-performing models and their corresponding input data combinations (C1, C2, C3, and C4), optimized across the 10 iterations of the “Leave-Some-Subjects-Out” (LSSO) cross-validation procedure.

Model	Hyperparameter	Value
C1: ETC	n_estimators	235
	max_features	23
	max_depth	7
	class_weight	‘balanced’
C2: ETC	n_estimators	123
	max_features	7
	max_depth	8
	class_weight	‘balanced’
C3: SVC	C	82.6
	gamma	1.67 × 10−4
	kernel	‘rbf’
C4: SVC	C	82.6
	gamma	1.67 × 10−4
	kernel	‘rbf’

C1, C2, C3, C4 = input features combinations; ETC = extra trees classifier; SVC = support vector classifier; n_estimators = number of trees; max_features = maximum features to consider; max_depth = maximum depth of the tree; class_weight = weight associated with classes; C = regularization parameter; gamma = kernel coefficient; kernel = kernel type.