Table 6.

Results of machine learning classifiers using hybrid features with CTGAN.

Model	Class	Precision	Recall	F1 Score	Model	Class	Precision	Recall	F1 Score
DT	0	0.89	0.90	0.89	LR	0	0.89	0.91	0.90
	1	0.99	0.99	0.99		1	1.00	1.00	1.00
	2	0.90	0.89	0.89		2	0.91	0.90	0.90
	Micro avg.	0.93	0.93	0.93		Micro avg.	0.93	0.93	0.93
	Weighted avg.	0.93	0.93	0.93		Weighted avg.	0.93	0.93	0.93
	Accuracy	0.93				Accuracy	0.93
SVC	0	0.90	0.95	0.93	NB	0	0.37	0.77	0.50
	1	1.00	1.00	1.00		1	0.74	0.50	0.60
	2	0.96	0.91	0.93		2	0.46	0.16	0.24
	Micro avg.	0.95	0.95	0.95		Micro avg.	0.52	0.48	0.45
	Weighted avg.	0.95	0.95	0.95		Weighted avg.	0.53	0.47	0.45
	Accuracy	0.95				Accuracy	0.47
KNN	0	0.72	0.89	0.79	RF	0	0.96	0.92	0.94
	1	0.98	0.95	0.93		1	1.00	1.00	1.00
	2	0.88	0.72	0.79		2	0.93	0.97	0.95
	Micro avg.	0.85	0.85	0.85		Micro avg.	0.97	0.96	0.96
	Weighted avg.	0.85	0.85	0.85		Weighted avg.	0.97	0.97	0.97
	Accuracy	0.85				Accuracy	0.97
Hard Voting	0	0.89	0.94	0.92	Soft Voting	0	0.87	0.93	0.90
	1	1.00	1.00	1.00		1	1.00	1.00	1.00
	2	0.95	0.90	0.92		2	0.94	0.88	0.91
	Micro avg.	0.95	0.95	0.95		Micro avg.	0.94	0.94	0.94
	Weighted avg.	0.95	0.95	0.95		Weighted avg.	0.94	0.94	0.94
	Accuracy	0.95				Accuracy	0.94