Summary of evaluation metrics for machine learning predictive models during repeated internal 10-fold cross-validation
| Model | AUC (mean) (maximum) | TPR (mean) (maximum) | FPR (mean) (minimum) | Misclassification Error (mean) (minimum) |
|---|---|---|---|---|
| BG | 0.84 (0.91) | 0.75 (0.84) | 0.23 (0.18) | 0.23 (0.14) |
| RF | 0.84 (0.92) | 0.78 (0.89) | 0.25 (0.17) | 0.25 (0.16) |
| SVM | 0.85 (0.90) | 0.82 (0.95) | 0.26 (0.16) | 0.23 (0.17) |
| KNN | 0.82 (0.90) | 0.74 (0.90) | 0.23 (0.14) | 0.23 (0.15) |
| LR | 0.83 (0.92) | 0.73 (0.95) | 0.23 (0.15) | 0.24 (0.16) |
Note:—AUC indicates area under the curve; BG, bagging or bootstrap aggregating; FPR, false-positive rate; KNN, K-nearest neighbor; LR, logistic regression; RF, random forest; SVM, support vector machine; TPR, true-positive rate.