Table 3.

Model Parameters.

Model	Parameter	Value
Logistic regression	Norm	L2
	Formulation	Primal
	Inverse of regularization	10^–15
	Bias constant added	Yes
	Bias constant scaling	1
	Solver	liblinear
	Tolerance	10^-4
Random forest	Tree count	100
	Quality measure	Gini impurity
	Samples per split	2
	Min samples per leaf	1
	Min impurity split	10^-7
Parameters in Common	NF	5
	N	36 (28 R−, 8 R+)
	R- probability threshold	0.8
	Validation	LOOCV

Parameters used to train machine learning models. The threshold for non-responder classification was selected as 0.8; the probability of the patient being a non-responder must be determined to be greater than 0.8 by the model, otherwise a responder label is applied. Model implementations were enabled by the freely available scikit-learn Python library.

Abbreviations: number of features, NF; number of patients, N; treatment non-responder, R−; treatment responder, R+; leave-one-out cross-validation, LOOCV