An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

Table 4

The prediction result of GE-SVM_AT for the first data fold.

Case	IF1	IF2	IF3	IF4	IF5	IF6	IF7	Actual; result	Predicted; result
1	0.0021	0.0024	0.2798	0.0182	0.9274	0.9935	0.5038	−1	−1
2	0.1048	0.1026	0.0111	0.2391	0.4918	0.6257	1.0000	−1	−1
3	0.0000	0.0000	0.8934	0.0193	0.9419	0.9935	1.0000	−1	−1
4	0.1519	0.2468	0.0110	0.1602	0.4482	0.1942	0.5038	+1	+1
5	0.5995	0.5593	0.0041	0.0300	0.4337	0.1079	1.0000	+1	+1
6	0.0236	0.0192	0.0140	0.0127	0.8693	0.9935	1.0000	−1	−1
7	0.0094	0.0088	0.0807	0.0131	0.8548	0.9924	1.0000	−1	−1
8	0.2462	0.3189	0.0062	0.0906	0.5208	0.1877	0.5038	+1	+1
9	0.1284	0.1907	0.0127	0.1162	0.5789	0.3074	0.5038	+1	+1
10	0.8469	0.7196	0.0045	0.0369	0.3611	0.0367	1.0000	+1	+1
11	0.0836	0.0865	0.0203	0.1790	0.5498	0.3139	0.5038	+1	−1
12	0.0001	0.0002	1.0000	1.0000	0.7096	0.9342	0.5038	−1	−1
13	0.1284	0.1747	0.0128	0.1606	0.4627	0.2567	0.5038	+1	+1
14	0.2462	0.2468	0.0070	0.0815	0.4192	0.2147	0.5038	+1	+1
15	0.0130	0.0144	0.0402	0.0290	0.7967	0.9827	1.0000	−1	−1
16	0.0130	0.0112	0.0452	0.0156	0.8403	0.9881	0.5038	−1	−1
17	0.0931	0.0946	0.0181	0.2299	0.4192	0.2524	1.0000	+1	+1
18	0.8469	0.6955	0.0052	0.0221	0.2739	0.0410	0.5038	+1	+1
19	0.5642	0.5673	0.0097	0.0461	0.4046	0.1003	0.5038	+1	+1
20	0.0389	0.0417	0.0309	0.0000	0.5353	0.7843	0.5038	−1	−1
21	0.2226	0.2548	0.0064	0.1039	0.3756	0.2082	0.5038	+1	+1
22	0.8233	0.6394	0.0052	0.0182	0.3030	0.0496	0.5038	+1	+1
23	0.2108	0.2468	0.0066	0.1188	0.4918	0.1942	1.0000	+1	+1
24	0.8704	0.7516	0.0050	0.0289	0.3175	0.0270	0.5038	+1	+1