Table 3 Bayesian optimization search space and optimal values for hyperparameters in extreme gradient boosting (XGBoost) and random forest (RF) efficacy prediction models.
Classifier models | Hyperparameters | Optimal value | Search space |
|---|---|---|---|
Extreme gradient boosting | learning rate | 0.351 | 0.01,0.5 |
n estimators | 401 | 10,500 | |
gamma | 0.315 | 0.01,1 | |
reg alpha | 0.18 | 0.01,1 | |
reg lambda | 0.184 | 0.01,1 | |
Random forest | n estimators | 677 | 1, 1000 |
min samples split | 2 | 2,10 | |
min samples leaf | 5 | 1,5 | |
min weight fraction leaf | 0.0511 | 0,0.5 | |
min impurity decrease | 0.1428 | 0,1 | |
max samples | 0.5451 | 0.1,1 | |
max depth | 97 | 1,100 |
