Table 1.

The performance of classification models developed on Dataset_1 for discriminating B3PPs and CPPs; models were developed using different machine learning techniques.

Methods	Training Dataset_1					Validation Dataset_1
	Sens	Spec	Acc	AUROC	MCC	Sens	Spec	Acc	AUROC	MCC
RF	86.04	84.18	82.09	0.90	0.64	75.92	87.03	81.48	0.88	0.63
XGB	81.39	82.32	80.93	0.88	0.62	79.63	88.89	84.25	0.88	0.68
LR	82.79	83.25	83.48	0.90	0.67	81.48	87.03	84.26	0.91	0.69
SVC	83.25	82.79	81.86	0.88	0.64	74.07	92.59	83.33	0.91	0.67
KNN	66.51	64.65	65.58	0.74	0.32	48.18	77.77	62.93	0.72	0.27
GNB	84.18	82.32	80	0.86	0.61	53.70	94.44	74.07	0.86	0.52
DT	78.14	75.34	73.49	0.79	0.47	74.07	70.37	72.22	0.76	0.44