Table 2 The evaluation indicators for each model
Model | Balanced accuracy | Recall | Precision | F1 score |
|---|---|---|---|---|
Decision Tree | 80.46%(78.57–89.92%) | 87.04%(80.00-98.12%) | 50.31%(43.74–67.23%) | 63.73%(57.73–78.26%) |
SVM | 83.20%(76.31–90.14%) | 82.66%(67.65–91.67%) | 60.79%(51.77–78.44%) | 69.94%(59.99–80.41%) |
Random Forest | 82.75%(81.94–91.81%) | 88.00%(84.00-100.00%) | 54.25%(47.83–73.02%) | 67.09%(63.41–81.63%) |
XGBoost | 82.49%(78.79–91.90%) | 82.69%(70.58–94.12%) | 58.85%(55.17–80.44%) | 68.61%(63.63–83.73%) |
AdaBoost | 77.49%(70.67–85.78%) | 59.63%(47.22–75.02%) | 79.39%(66.67–92.59%) | 68.07%(56.60–80.00%) |