Table 6.

The MAE and MRE of nonlinear regression models and artificial neural networks (ANN) models in testing phase^1,2

Items, MJ/d	n	RMSE		MAE		MRE
		Regression	ANN	Regression	ANN	Regression	ANN
ME intake	30	4.46	3.23	3.56	2.64	11.39	7.99
NE intake	30	4.24	2.95	3.47	2.15	12.45	8.30
NEm	14	0.67	0.45	0.82	0.52	7.75	5.19
NEp	30	0.74	0.51	0.6	0.39	14.02	8.80
NEl	14	0.78	0.58	0.93	0.63	15.21	8.11

¹The RMSE, MAE, and MRE were calculated using the predicted value and observed value in the testing data set which was not used to establish the prediction models.

²RMSE, root mean square error; MAE, mean absolute error; MRE, mean relative error; NE, net energy, NE_m, NE for maintenance; NE_p, NE retained as protein; NE_l, NE retained as lipid.