Table 5.

Hyper-parameters values explored in the machine learning tools applied for development of the non-linear mt-QSAR models.

Method	Parameters Tuned	Parameters Selected	10-Fold CV Accuracy (%) a
RF	Bootstrap: True/False	False
	Criterion: Gini, Entropy,	Gini
	Maximum depth: 10, 30, 50, 70, 90, 100, None	90
	Maximum features: Auto, Sqrt	Sqrt	91.02
	Minimum samples leaf: 1, 2, 4	1
	Minimum samples split: 2, 5, 10	5
	Number of estimators: 50, 100, 200,500	200
kNN	Number of neighbors: 1–31	20
	Weight options: Uniform, Distance	Distance	79.20
	Algorithms: Auto, Ball tree, kd_tree, brute	Auto
Xgboost	Minimum child weight: 1,5,10	1
	Gamma: 0, 0.5, 1, 1.5, 2, 5	0
	Sum sample: 0.6, 0.8, 1.0	0.8	91.54
	Number of estimators: 50, 100, 200,300	100
	Maximum depth: 3, 4, 5	5
RBF-SVC	C: 0.1, 1, 10, 100, 1000	1	62.30
	Gamma: 1, 0.1, 0.01, 0.001	1
MLP	Hidden layer sizes:(50,50,50), (50,100,50), (100,)	(100,)
	Activation: Identity, Logistic, Tanh, Relu	Relu
	Solver: SGD, Adam	Adam	82.97
	Alpha: 0.0001, 0.001, 0.01,1	0.0001
	Learning rate: Constant, Adaptive, Inverse scaling	Adaptive
DT	Criterion: Gini, EntropyMaximum depth: 10,30,50,70,90,100, NoneMaximum features: Auto, SqrtMinimum samples leaf: 1,2,4Minimum samples split: 2–50	Entropy100Sqrt113	84.33
NB	Alpha: 1,0.5,0.1Fit prior: True, False	0.1True	69.40

^a The cross-validation accuracy was estimated only on the sub-training set.