A Prediction Model Optimization Critiques through Centroid Clustering by Reducing the Sample Size, Integrating Statistical and Machine Learning Techniques for Wheat Productivity

Table 3

Integrating the DTR and RFR with evaluation metric and information criteria.

Datasets	Machine learning DTR						Machine learning RFR
	P.Score	RMSE	AIC	ΔAICi	AICWi	E.R	P.Score	RMSE	AIC	ΔAICi	AICWi	E.R
D1	0.364 (0.323)	8.51 (8.82)	4.28	4.00	0.07	7.37	0.380 (0.345)	8.40 (8.68)	4.26	3.56	0.09	5.92
D2	0.366 (0.331)	7.22 (7.82)	3.96	3.67	0.13	6.27	0.388 (0.362)	7.09 (7.64)	3.92	3.22	0.11	5.00
D3	0.940 (0.731)	1.92 (4.26)	1.44	1.15	0.30	1.78	0.948 (0.786)	1.78 (3.79)	2.18	1.48	0.26	2.10
D4	0.987 (0.741)	0.828 (2.87)	0.29		0.54		0.973 (0.877)	1.23 (1.97)	0.70		0.54

Testing dataset values are shown in parenthesis.