Table 1 Summary of the values of the hyperparameters for the best RF, SGB, NB and XGBoost models
Methods | Hyperparameters | Best Value | Meaning |
|---|---|---|---|
RF | mtry | 8 | The number of random features used in each tree |
ntree | 500 | The number of trees in forest | |
XGBoost | nrounds | 100 | The number of tree model iterations |
max_depth | 3 | The maximum depth of a tree | |
eta | 0.4 | Shrinkage coefficient of tree | |
gamma | 0 | The minimum loss reduction | |
subsample | 0.75 | Subsample ratio of columns when building each tree | |
colsample_bytree | 0.8 | Subsample ratio of columns when constructing each tree | |
rate_drop | 0.5 | Rate of trees dropped | |
skip_drop | 0.05 | Probability of skipping the dropout procedure during a boosting iteration | |
min_child_weight | 1 | The minimum sum of instance weight | |
NB | fL | 0 | Adjustment of Laplace smoother |
usekernel | TRUE | Using kernel density estimate for continuous variable versus a Gaussian density estimate | |
adjust | 1 | Adjust the bandwidth of the kernel density | |
SGB | n.trees | 50 | The number of tree model iterations |
interaction.depth | 1 | The iterations depth of a tree | |
shrinkage | 0.1 | Subsample ratio of columns when building each tree | |
n.minobsinnode | 10 | The minimum number of instances per leaf Node |