Table 2.
The confusion matrix of k-NN model
|
Target Class (Training)
|
Target Class (Test)
|
||||||||
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | All | 0 | 1 | All | ||||
|
Output Class
(Training) |
0 | 16 40% |
0 0% |
100% P 0% F |
Output Class
(Test) |
0 | 5 31% |
0 0% |
100% P 0% F |
| 1 | 2 5% |
22 55% |
92% P 8% F |
1 | 2 13% |
9 56% |
82% P 18% F |
||
| All | 89% P 11% F |
100% P 0% F |
95% P 5% F |
All | 71% P 29% F |
100% P 0% F |
88% P 12% F |
||
0 = adulterated samples, 1 = genuine samples, P = pass, F = fail, Output class = k-NN output class, Target class = Real data class.