モデルをエクスポートした後に元データと予測結果からtableを作ればできるかと思います。
ドキュメントの例に含まれているフィッシャーのアヤメデータを使ってサンプルを書きます。
t = readtable('fisheriris.csv');
% ID列を追加
t.ID = (1:height(t))';
% 分類学習器を起動
% classificationLearner
% 学習させたモデルを「コンパクトモデルのエクスポート」でcompactTrainedModelという変数でワークスペースで保存
% ここではmatファイルに出力したものを読み込みます
load compactTrainedModel
yPred = compactTrainedModel.predictFcn(t);
resultTable = table(t.ID, t.Species, yPred, 'VariableNames', {'ID', '分類ラベル', '予測結果'});
disp(resultTable)
ID 分類ラベル 予測結果
___ ______________ ______________
1 {'setosa' } {'setosa' }
2 {'setosa' } {'setosa' }
3 {'setosa' } {'setosa' }
4 {'setosa' } {'setosa' }
5 {'setosa' } {'setosa' }
6 {'setosa' } {'setosa' }
7 {'setosa' } {'setosa' }
8 {'setosa' } {'setosa' }
9 {'setosa' } {'setosa' }
10 {'setosa' } {'setosa' }
11 {'setosa' } {'setosa' }
12 {'setosa' } {'setosa' }
13 {'setosa' } {'setosa' }
14 {'setosa' } {'setosa' }
15 {'setosa' } {'setosa' }
16 {'setosa' } {'setosa' }
17 {'setosa' } {'setosa' }
18 {'setosa' } {'setosa' }
19 {'setosa' } {'setosa' }
20 {'setosa' } {'setosa' }
21 {'setosa' } {'setosa' }
22 {'setosa' } {'setosa' }
23 {'setosa' } {'setosa' }
24 {'setosa' } {'setosa' }
25 {'setosa' } {'setosa' }
26 {'setosa' } {'setosa' }
27 {'setosa' } {'setosa' }
28 {'setosa' } {'setosa' }
29 {'setosa' } {'setosa' }
30 {'setosa' } {'setosa' }
31 {'setosa' } {'setosa' }
32 {'setosa' } {'setosa' }
33 {'setosa' } {'setosa' }
34 {'setosa' } {'setosa' }
35 {'setosa' } {'setosa' }
36 {'setosa' } {'setosa' }
37 {'setosa' } {'setosa' }
38 {'setosa' } {'setosa' }
39 {'setosa' } {'setosa' }
40 {'setosa' } {'setosa' }
41 {'setosa' } {'setosa' }
42 {'setosa' } {'setosa' }
43 {'setosa' } {'setosa' }
44 {'setosa' } {'setosa' }
45 {'setosa' } {'setosa' }
46 {'setosa' } {'setosa' }
47 {'setosa' } {'setosa' }
48 {'setosa' } {'setosa' }
49 {'setosa' } {'setosa' }
50 {'setosa' } {'setosa' }
51 {'versicolor'} {'versicolor'}
52 {'versicolor'} {'versicolor'}
53 {'versicolor'} {'versicolor'}
54 {'versicolor'} {'versicolor'}
55 {'versicolor'} {'versicolor'}
56 {'versicolor'} {'versicolor'}
57 {'versicolor'} {'versicolor'}
58 {'versicolor'} {'versicolor'}
59 {'versicolor'} {'versicolor'}
60 {'versicolor'} {'versicolor'}
61 {'versicolor'} {'versicolor'}
62 {'versicolor'} {'versicolor'}
63 {'versicolor'} {'versicolor'}
64 {'versicolor'} {'versicolor'}
65 {'versicolor'} {'versicolor'}
66 {'versicolor'} {'versicolor'}
67 {'versicolor'} {'versicolor'}
68 {'versicolor'} {'versicolor'}
69 {'versicolor'} {'versicolor'}
70 {'versicolor'} {'versicolor'}
71 {'versicolor'} {'virginica' }
72 {'versicolor'} {'versicolor'}
73 {'versicolor'} {'versicolor'}
74 {'versicolor'} {'versicolor'}
75 {'versicolor'} {'versicolor'}
76 {'versicolor'} {'versicolor'}
77 {'versicolor'} {'versicolor'}
78 {'versicolor'} {'virginica' }
79 {'versicolor'} {'versicolor'}
80 {'versicolor'} {'versicolor'}
81 {'versicolor'} {'versicolor'}
82 {'versicolor'} {'versicolor'}
83 {'versicolor'} {'versicolor'}
84 {'versicolor'} {'virginica' }
85 {'versicolor'} {'versicolor'}
86 {'versicolor'} {'versicolor'}
87 {'versicolor'} {'versicolor'}
88 {'versicolor'} {'versicolor'}
89 {'versicolor'} {'versicolor'}
90 {'versicolor'} {'versicolor'}
91 {'versicolor'} {'versicolor'}
92 {'versicolor'} {'versicolor'}
93 {'versicolor'} {'versicolor'}
94 {'versicolor'} {'versicolor'}
95 {'versicolor'} {'versicolor'}
96 {'versicolor'} {'versicolor'}
97 {'versicolor'} {'versicolor'}
98 {'versicolor'} {'versicolor'}
99 {'versicolor'} {'versicolor'}
100 {'versicolor'} {'versicolor'}
101 {'virginica' } {'virginica' }
102 {'virginica' } {'virginica' }
103 {'virginica' } {'virginica' }
104 {'virginica' } {'virginica' }
105 {'virginica' } {'virginica' }
106 {'virginica' } {'virginica' }
107 {'virginica' } {'virginica' }
108 {'virginica' } {'virginica' }
109 {'virginica' } {'virginica' }
110 {'virginica' } {'virginica' }
111 {'virginica' } {'virginica' }
112 {'virginica' } {'virginica' }
113 {'virginica' } {'virginica' }
114 {'virginica' } {'virginica' }
115 {'virginica' } {'virginica' }
116 {'virginica' } {'virginica' }
117 {'virginica' } {'virginica' }
118 {'virginica' } {'virginica' }
119 {'virginica' } {'virginica' }
120 {'virginica' } {'virginica' }
121 {'virginica' } {'virginica' }
122 {'virginica' } {'virginica' }
123 {'virginica' } {'virginica' }
124 {'virginica' } {'virginica' }
125 {'virginica' } {'virginica' }
126 {'virginica' } {'virginica' }
127 {'virginica' } {'virginica' }
128 {'virginica' } {'virginica' }
129 {'virginica' } {'virginica' }
130 {'virginica' } {'virginica' }
131 {'virginica' } {'virginica' }
132 {'virginica' } {'virginica' }
133 {'virginica' } {'virginica' }
134 {'virginica' } {'virginica' }
135 {'virginica' } {'virginica' }
136 {'virginica' } {'virginica' }
137 {'virginica' } {'virginica' }
138 {'virginica' } {'virginica' }
139 {'virginica' } {'virginica' }
140 {'virginica' } {'virginica' }
141 {'virginica' } {'virginica' }
142 {'virginica' } {'virginica' }
143 {'virginica' } {'virginica' }
144 {'virginica' } {'virginica' }
145 {'virginica' } {'virginica' }
146 {'virginica' } {'virginica' }
147 {'virginica' } {'virginica' }
148 {'virginica' } {'virginica' }
149 {'virginica' } {'virginica' }
150 {'virginica' } {'virginica' }
