Table 8 Model performance results with... | Emerald Publishing

Table 8

Model performance results with GenAI as a dependent variable

Sampling method	Model	Accuracy	Precision	Recall	F1 score	RMSE	NRMSE
Original	Logistic regression	0.30	0.28	0.30	0.29	1.25	0.42
	Decision tree	0.30	0.27	0.30	0.28	1.38	0.46
	Random forest	0.33	0.32	0.33	0.30	1.19	0.40
Oversampling	Logistic regression	0.30	0.29	0.30	0.29	1.29	0.43
	Decision tree	0.36	0.37	0.36	0.35	1.38	0.46
	Random forest	0.30	0.30	0.30	0.29	1.17	0.39
Undersampling	Logistic regression	0.28	0.27	0.28	0.27	1.37	0.46
	Decision tree	0.26	0.27	0.26	0.26	1.37	0.46
	Random forest	0.36	0.37	0.36	0.36	1.23	0.41

Sampling method	Model	Accuracy	Precision	Recall	F1 score	RMSE	NRMSE
Original	Logistic regression	0.30	0.28	0.30	0.29	1.25	0.42
	Decision tree	0.30	0.27	0.30	0.28	1.38	0.46
	Random forest	0.33	0.32	0.33	0.30	1.19	0.40
Oversampling	Logistic regression	0.30	0.29	0.30	0.29	1.29	0.43
	Decision tree	0.36	0.37	0.36	0.35	1.38	0.46
	Random forest	0.30	0.30	0.30	0.29	1.17	0.39
Undersampling	Logistic regression	0.28	0.27	0.28	0.27	1.37	0.46
	Decision tree	0.26	0.27	0.26	0.26	1.37	0.46
	Random forest	0.36	0.37	0.36	0.36	1.23	0.41

Note(s): RMSE = Root mean square error. NRMSE = Normalized root mean square error

Source(s): Authors' own compilation