Table 5 Classifiers and their Accuracy, Precision, Recall and F1-score after Normalization.
Classifier | Image Matrix | Accuracy | Precision | Recall | F1-Score |
|---|---|---|---|---|---|
SVM | 64 | 88% | 0.82 | 0.88 | 0.85 |
128 | 89% | 0.84 | 0.85 | 0.85 | |
256 | 90% | 0.88 | 0.89 | 0.89 | |
KNN | 64 | 82% | 0.73 | 0.87 | 0.79 |
128 | 82% | 0.77 | 0.77 | 0.77 | |
256 | 84% | 0.86 | 0.74 | 0.79 | |
RF | 64 | 90% | 0.88 | 0.90 | 0.90 |
128 | 91% | 0.89 | 0.89 | 0.90 | |
256 | 94% | 0.90 | 0.90 | 0.91 | |
XGBoost | 64 | 92% | 0.88 | 0.88 | 0.89 |
128 | 94% | 0.91 | 0.92 | 0.92 | |
256 | 95% | 0.90 | 0.92 | 0.92 |
