Table 1.
Testing results from six different classifier models for wine samples.
| Classifier | Type | Sensitivity | Specificity | PPV | NPV | Accuracy |
|---|---|---|---|---|---|---|
| Linear discriminant |
Zinfandel | 1 | 1 | 1 | 1 | 1 |
| Cabernet sauvignon | 1 | 1 | 1 | 1 | 1 | |
| Pinot noir | 1 | 0.8889 | 1 | 0.9643 | 0.9722 | |
| Merlot | 0.963 | 1 | 0.9 | 1 | 0.9722 | |
| Quadratic discriminant |
Zinfandel | 1 | 1 | 1 | 1 | 1 |
| Cabernet sauvignon | 1 | 1 | 1 | 1 | 1 | |
| Pinot noir | 1 | 0.5 | 1 | 0.8571 | 0.875 | |
| Merlot | 0.833 | 1 | 0.6667 | 1 | 0.875 | |
| Linear SVM |
Zinfandel | 1 | 1 | 1 | 1 | 1 |
| Cabernet sauvignon | 1 | 1 | 1 | 1 | 1 | |
| Pinot noir | 1 | 0.944 | 1 | 0.9818 | 0.9861 | |
| Merlot | 0.9815 | 1 | 0.9474 | 1 | 0.9861 | |
| Quadratic SVM |
Zinfandel | 1 | 1 | 1 | 1 | 1 |
| Cabernet sauvignon | 1 | 1 | 1 | 1 | 1 | |
| Pinot noir | 1 | 0.944 | 1 | 0.9818 | 0.9861 | |
| Merlot | 0.9815 | 1 | 0.9474 | 1 | 0.9861 | |
| Bayes Gaussian |
Zinfandel | 1 | 1 | 1 | 1 | 1 |
| Cabernet sauvignon | 1 | 0.5 | 1 | 0.8571 | 0.875 | |
| Pinot noir | 1 | 0.833 | 1 | 0.9474 | 0.9583 | |
| Merlot | 0.7778 | 1 | 0.6 | 1 | 0.8333 | |
| KNN fine |
Zinfandel | 1 | 1 | 1 | 1 | 1 |
| Cabernet sauvignon | 1 | 0.5 | 1 | 0.8571 | 0.875 | |
| Pinot noir | 1 | 0.9444 | 1 | 0.9818 | 0.9861 | |
| Merlot | 0.8148 | 1 | 0.6429 | 1 | 0.8611 |