Estimating Concrete Workability Based on Slump Test with Least Squares Support Vector Regression

Table 4

The result of the 10-fold cross-validation process.

Data fold		1	2	3	4	5	6	7	8	9	10	Avg.
CSP-LSSVR	Training
	RMSE	0.46	0.32	0.24	0.30	0.25	0.29	0.29	0.26	0.69	0.29	0.34
	MAPE	3.15	1.37	0.65	1.13	0.68	0.90	0.88	0.96	5.21	0.92	1.59
		0.97	0.99	0.99	0.99	0.99	0.99	0.99	0.99	0.94	0.99	0.98
	Testing
	RMSE	0.68	0.33	0.64	0.46	0.58	0.39	0.38	0.67	0.49	0.37	0.50
	MAPE	4.50	2.29	2.22	2.60	2.69	1.70	2.16	3.51	3.92	2.52	2.81
		0.99	0.83	1.00	0.58	0.98	0.99	0.99	0.97	0.97	0.72	0.90
ANN	Training
	RMSE	0.56	0.47	0.48	0.74	0.57	0.61	0.36	0.50	0.44	0.46	0.52
	MAPE	4.27	3.13	3.45	3.00	3.78	4.25	1.56	3.43	2.56	2.36	3.18
		0.96	0.97	0.96	0.94	0.96	0.96	0.98	0.97	0.98	0.97	0.97
	Testing
	RMSE	0.62	0.53	0.71	1.10	0.51	0.58	0.40	0.83	0.31	0.63	0.62
	MAPE	4.97	5.21	4.33	6.30	3.58	4.11	2.38	6.68	1.65	5.17	4.44
		0.98	0.60	0.99	0.09	0.97	0.98	0.98	0.91	0.99	0.29	0.78
MLR	Training
	RMSE	1.23	1.25	1.19	1.20	1.27	1.21	1.25	1.27	1.29	1.23	1.24
	MAPE	9.26	9.56	9.07	9.29	9.73	9.16	9.80	9.85	10.12	9.62	9.55
		0.79	0.81	0.75	0.83	0.80	0.79	0.79	0.79	0.79	0.82	0.80
	Testing
	RMSE	1.56	1.28	1.78	1.67	1.09	1.58	1.28	1.04	0.78	1.54	1.36
	MAPE	11.56	11.10	11.59	11.93	9.44	14.40	10.53	8.10	5.67	12.08	10.64
		0.90	0.01	0.91	0.01	0.80	0.82	0.79	0.85	0.90	0.28	0.63

Note: Avg. denotes the average result.