Table 3.

The performance of classification models developed on Dataset_3 for discriminating B3PPs and CPPs; models were developed using different machine learning techniques.

Methods	Training Dataset_3					Validation Dataset_3
	Sens	Spec	Acc	AUROC	MCC	Sens	Spec	Acc	AUROC	MCC
RF	86.97	85.08	85.25	0.93	0.51	81.48	83.08	82.93	0.90	0.44
XGB	72.55	93.82	91.88	0.92	0.58	72.22	92.00	90.20	0.892	0.52
LR	80.93	89.73	88.93	0.92	0.54	83.33	89.40	88.85	0.93	0.55
SVC	80.00	84.75	84.32	0.90	0.45	85.18	82.15	82.43	0.90	0.45
KNN	83.72	80.76	81.03	0.88	0.43	79.63	78.44	78.54	0.84	0.37
GNB	80.46	75.74	76.20	0.84	0.35	83.33	72.67	73.65	0.86	0.34
DT	85.11	65.00	66.83	0.82	0.30	64.82	63.20	63.40	0.72	0.20