KNN (k=8) Classifier
1495 samples, 75 errors
94.98 % hit rate, 5.02 % error rate
classification Matrix:
|
obj. 1 |
obj. 2 |
obj. 3 |
obj. 4 |
obj. 5 |
obj. 6 |
obj. 7 |
obj. 8 |
obj. 9 |
obj. 10 |
classified as obj. 1 |
134 |
0 |
0 |
1 |
3 |
0 |
0 |
0 |
3 |
0 |
classified as obj. 2 |
0 |
125 |
0 |
0 |
0 |
0 |
3 |
2 |
0 |
0 |
classified as obj. 3 |
2 |
1 |
143 |
0 |
7 |
4 |
5 |
6 |
1 |
1 |
classified as obj. 4 |
2 |
0 |
0 |
144 |
0 |
0 |
2 |
0 |
2 |
0 |
classified as obj. 5 |
1 |
0 |
0 |
0 |
155 |
0 |
0 |
1 |
1 |
0 |
classified as obj. 6 |
1 |
0 |
0 |
1 |
0 |
133 |
3 |
2 |
1 |
0 |
classified as obj. 7 |
0 |
0 |
0 |
0 |
1 |
0 |
115 |
0 |
1 |
1 |
classified as obj. 8 |
0 |
0 |
1 |
2 |
2 |
0 |
0 |
187 |
3 |
0 |
classified as obj. 9 |
0 |
1 |
0 |
2 |
0 |
0 |
0 |
2 |
118 |
0 |
classified as obj. 10 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
0 |
0 |
166 |
LaTeX
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Mahalanobis Classifier
1495 samples, 161 errors
89.23 % hit rate, 10.77 % error rate
classification Matrix:
|
obj. 1 |
obj. 2 |
obj. 3 |
obj. 4 |
obj. 5 |
obj. 6 |
obj. 7 |
obj. 8 |
obj. 9 |
obj. 10 |
classified as obj. 1 |
128 |
1 |
0 |
1 |
0 |
5 |
1 |
0 |
0 |
2 |
classified as obj. 2 |
0 |
120 |
9 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
classified as obj. 3 |
0 |
0 |
63 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
classified as obj. 4 |
2 |
0 |
6 |
147 |
1 |
1 |
0 |
1 |
2 |
0 |
classified as obj. 5 |
6 |
5 |
45 |
0 |
167 |
8 |
4 |
4 |
2 |
3 |
classified as obj. 6 |
0 |
0 |
0 |
0 |
0 |
107 |
0 |
0 |
0 |
0 |
classified as obj. 7 |
3 |
1 |
4 |
0 |
0 |
4 |
126 |
0 |
1 |
3 |
classified as obj. 8 |
0 |
0 |
8 |
2 |
0 |
10 |
0 |
195 |
3 |
1 |
classified as obj. 9 |
1 |
0 |
2 |
0 |
0 |
0 |
0 |
0 |
122 |
0 |
classified as obj. 10 |
0 |
0 |
7 |
0 |
0 |
1 |
0 |
0 |
0 |
159 |
LaTeX
|
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