Models | AUC | 95%CI | Sensitivity (recall) | Specificity | Accuracy | log-loss | FP rate | Precision | AP | F1 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Lower bound | Upper bound | ||||||||||
LR | 0.873 | 0.808 | 0.939 | 0.83 | 0.82 | 0.82 | 6.16 | 0.18 | 0.76 | 0.83 | 0.79 |
Elastic Net | 0.871 | 0.805 | 0.937 | 0.85 | 0.80 | 0.82 | 6.29 | 0.20 | 0.74 | 0.82 | 0.79 |
Lasso | 0.872 | 0.807 | 0.938 | 0.84 | 0.79 | 0.81 | 6.41 | 0.21 | 0.74 | 0.82 | 0.79 |
Ridge | 0.865 | 0.798 | 0.933 | 0.83 | 0.79 | 0.81 | 6.71 | 0.21 | 0.73 | 0.82 | 0.78 |
SVM | 0.857 | 0.786 | 0.928 | 0.82 | 0.81 | 0.81 | 6.50 | 0.19 | 0.75 | 0.82 | 0.78 |
RF | 0.854 | 0.782 | 0.926 | 0.83 | 0.79 | 0.80 | 6.77 | 0.21 | 0.73 | 0.81 | 0.77 |
k-NN | 0.802 | 0.721 | 0.883 | 0.74 | 0.74 | 0.74 | 8.91 | 0.26 | 0.69 | 0.73 | 0.70 |
NN | 0.854 | 0.783 | 0.925 | 0.83 | 0.78 | 0.80 | 6.91 | 0.22 | 0.73 | 0.80 | 0.77 |
XGBoost | 0.868 | 0.799 | 0.938 | 0.83 | 0.83 | 0.83 | 5.87 | 0.17 | 0.77 | 0.83 | 0.80 |