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

Table 8

RRSE and 𝑅 measures using the LAS on testing dataset.

Crop dataset	RRSE (%)				𝑅
	SLR	MLR	M5′	ANN	SLR	MLR	M5′	ANN
PJ01	203.86	81.90	58.00	75.25	0.87	0.81	0.90	0.82
CBP02	130.52	55.05	74.67	58.05	0.66	0.52	0.73	0.67
CBA03	98.76	136.96	112.45	58.40	0.64	0.76	−0.05	0.98
CBM04	479.43	306.29	85.30	78.96	−0.67	0.66	0.27	0.61
CP05	103.77	91.06	94.50	87.59	0.50	0.69	0.52	0.70
PA06	102.41	102.36	85.96	101.33	−0.42	−0.32	0.55	0.11
PA07	110.44	97.49	101.31	91.24	−0.06	0.67	0.09	0.45
TS08	112.85	86.59	82.40	137.48	0.42	0.69	0.64	0.69
Average (RRSE < 100)	98.76	82.41	80.14	74.92	0.50	0.67	0.60	0.71
Count (<100)	1	5	6	6
Average (all)	167.76	119.71	86.82	86.04	0.24	0.56	0.46	0.63