Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction

Table 3

RRSE, 𝑅, and RMAE measures using the OAS on testing dataset.

Crop dataset	RRSE (%)			𝑅			RMAE (%)
	MLR	M5′	ANN	MLR	M5′	ANN	MLR	M5′	ANN
PJ01	50.69	29.29	49.62	0.87	0.96	0.88	8.63	4.56	8.27
CBP02	52.14	58.85	58.05	0.67	0.68	0.67	5.67	6.40	6.41
CBA03	63.40	38.66	38.66	0.94	0.93	0.93	4.72	3.62	3.62
CBM04	70.53	71.20	75.04	0.69	0.59	0.58	1.30	1.59	1.58
CP05	87.83	83.52	87.59	0.72	0.65	0.70	8.13	6.39	8.46
PA06	95.28	74.02	86.16	−0.13	0.63	0.54	25.58	20.05	23.13
PA07	95.84	88.14	91.24	0.60	0.51	0.45	17.78	16.42	17.40
TS08	86.59	82.40	74.87	0.69	0.64	0.73	11.08	13.46	14.57
Average	75.29	65.76	70.15	0.63	0.70	0.72	10.36	9.06	10.43