Table 3 Performance measurements of developed ensemble models.

Model	Dataset	SSE	MAE	MSE	RMSE	Error	Accuracy	R²
Model	Dataset	–	kN/m²	(kN/m²)²	kN/m²	%	%	–
GB	Training	4.23	0.12	0.02	0.15	0.06	0.94	0.98
GB	Validation	0.68	0.09	0.01	0.12	0.05	0.95	0.99
CN2	Training	11.41	0.13	0.06	0.25	0.11	0.89	0.94
CN2	Validation	0.68	0.09	0.01	0.12	0.05	0.95	0.99
NB	Training	771.86	1.60	4.06	2.02	0.87	0.13	0.17
NB	Validation	186.07	1.51	3.72	1.93	0.86	0.14	0.41
SVM	Training	13.62	0.14	0.07	0.27	0.12	0.88	0.94
SVM	Validation	1.68	0.11	0.03	0.18	0.06	0.94	0.97
SGD	Training	123.27	0.59	0.65	0.81	0.35	0.65	0.35
SGD	Validation	26.68	0.57	0.03	0.73	0.33	0.67	0.51
KNN	Training	3.41	0.11	0.02	0.13	0.06	0.94	0.99
KNN	Validation	0.68	0.09	0.01	0.12	0.05	0.95	0.99
Tree	Training	8.96	0.13	0.05	0.22	0.09	0.91	0.95
Tree	Validation	0.68	0.09	0.01	0.12	0.05	0.95	0.99
RF	Training	43.54	0.21	0.23	0.48	0.21	0.79	0.78
RF	Validation	4.56	0.15	0.09	0.30	0.13	0.87	0.93

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