Table 5.
Model Name | Precision | Recall | Train Accuracy | Test Accuracy | F1 Score | AUC |
---|---|---|---|---|---|---|
Decision Tree | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.000000 |
Random Forest | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.999827 |
Logistic Regression | 0.94 | 0.98 | 0.97 | 0.97 | 0.96 | 0.971884 |
Ada Boost | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.000000 |
Bagging | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.000000 |
Gradient Boosting | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.000000 |
XGB | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.000000 |
SVC | 0.82 | 0.49 | 0.77 | 0.77 | 0.61 | 0.710974 |
K-Neighbors | 0.85 | 0.95 | 0.95 | 0.92 | 0.90 | 0.926795 |
Gaussian | 1.00 | 0.98 | 0.99 | 0.99 | 0.99 | 0.992494 |