Antibiotic | Model | Accuracy | Precision | Recall | f1_score | auc_roc |
---|---|---|---|---|---|---|
CIP | Logistic Regression | 0.85 | 0.60 | 0.48 | 0.54 | 0.83 |
Random Forest | 0.84 | 0.58 | 0.35 | 0.44 | 0.74 | |
SVM | 0.87 | 0.68 | 0.50 | 0.57 | 0.86 | |
Gradient Boosting | 0.86 | 0.72 | 0.33 | 0.46 | 0.83 | |
XGBoost | 0.85 | 0.63 | 0.41 | 0.49 | 0.82 | |
LightGBM | 0.85 | 0.61 | 0.43 | 0.50 | 0.84 | |
CatBoost | 0.87 | 0.81 | 0.39 | 0.52 | 0.84 | |
Feed-Forward NN (Keras) | 0.83 | 0.52 | 0.50 | 0.51 | 0.77 | |
AMP | Logistic Regression | 0.48 | 0.56 | 0.67 | 0.57 | 0.49 |
Random Forest | 0.43 | 0.52 | 0.57 | 0.54 | 0.44 | |
SVM | 0.51 | 0.56 | 0.71 | 0.63 | 0.47 | |
Gradient Boosting | 0.58 | 0.63 | 0.69 | 0.66 | 0.52 | |
XGBoost | 0.53 | 0.61 | 0.57 | 0.59 | 0.51 | |
LightGBM | 0.53 | 0.60 | 0.61 | 0.61 | 0.52 | |
CatBoost | 0.51 | 0.56 | 0.71 | 0.63 | 0.48 | |
Feed-Forward NN (Keras) | 0.52 | 0.58 | 0.67 | 0.62 | 0.47 | |
CTX | Logistic Regression | 0.91 | 0.47 | 0.38 | 0.42 | 0.77 |
Random Forest | 0.91 | 0.47 | 0.29 | 0.36 | 0.73 | |
SVM | 0.92 | 1.00 | 0.04 | 0.08 | 0.79 | |
Gradient Boosting | 0.91 | 0.33 | 0.12 | 0.18 | 0.82 | |
XGBoost | 0.91 | 0.47 | 0.29 | 0.36 | 0.81 | |
LightGBM | 0.92 | 0.57 | 0.33 | 0.42 | 0.81 | |
CatBoost | 0.91 | 0.33 | 0.12 | 0.18 | 0.80 | |
Feed-Forward NN (Keras) | 0.91 | 0.42 | 0.21 | 0.28 | 0.80 |