Table 1 Machine learning model performance comparison results.
Model | Accuracy | Precision | Recall | F1-Score | ROC-AUC | CV Accuracy | Weighted Score |
|---|---|---|---|---|---|---|---|
Logistic regression | 0.78 | 0.80 | 0.85 | 0.82 | 0.82 | 0.79 | 0.81 |
Decision tree | 0.74 | 0.76 | 0.80 | 0.78 | 0.76 | 0.75 | 0.76 |
Random forest | 0.81 | 0.82 | 0.89 | 0.85 | 0.84 | 0.81 | 0.84 |
SVM | 0.77 | 0.77 | 0.84 | 0.81 | 0.80 | 0.77 | 0.79 |
K-Nearest neighbors | 0.72 | 0.74 | 0.82 | 0.78 | 0.74 | 0.73 | 0.76 |
Gradient boosting | 0.80 | 0.81 | 0.88 | 0.84 | 0.82 | 0.79 | 0.82 |
AdaBoost | 0.76 | 0.77 | 0.83 | 0.80 | 0.79 | 0.77 | 0.79 |
Extra trees | 0.73 | 0.75 | 0.81 | 0.78 | 0.76 | 0.74 | 0.76 |
Naive bayes | 0.81 | 0.83 | 0.89 | 0.86 | 0.85 | 0.82 | 0.84 |
XGBoost | 0.85 | 0.86 | 0.92 | 0.89 | 0.89 | 0.85 | 0.88 |
