Table 2.
ML Model | Optimal hyperparameters | CV-AUC (95% CI) |
---|---|---|
XGBoost | Num. of trees = 149 Tree depth = 11 Minimum node size = 15 Num. of predictors = 452 Learning rate = 0.0673 Loss reduction = 4.315 |
0.933 (0.923-0.944) |
LASSO 1 | Penalty = 0.00044 | 0.930 (0.914-0.946) |
Random Forest | Num. of trees = 147 Num. of predictors = 53 Minimum node size = 26 |
0.916 (0.904-0.929) |
Abbreviations: AUC, area under the curve; CI, confidence interval; LASSO, least absolute shrinkage and selection operator; ML, machine learning; Num, number; XGBoost, eXtreme Gradient Boosting.
The penalty parameter for the LASSO model was a L1 (i.e., LASSO) penalty.