Train xgboost model with Bayesian optimalization. Code based on http://www.mysmu.edu/faculty/jwwang/post/hyperparameters-tuning-for-xgboost-using-bayesian-optimization/
Usage
OmicSelector_xgboost(
features = "all",
train = OmicSelector_load_datamix(use_smote_not_rose = T)[[1]],
test = OmicSelector_load_datamix(use_smote_not_rose = T)[[2]],
valid = OmicSelector_load_datamix(use_smote_not_rose = T)[[2]],
eta = c(0, 1),
gamma = c(0, 100),
max_depth = c(2L, 10L),
min_child_weight = c(1, 25),
subsample = c(0.25, 1),
nfold = c(3L, 10L),
initPoints = 8,
iters.n = 10
)Arguments
- features
Vector of features to be used. If "all", all features starting with 'hsa' will be used.
- train
Training dataset with column Class ('Case' vs. 'Control') and features starting with 'hsa'.
- test
Testing dataset with column Class ('Case' vs. 'Control') and features starting with 'hsa'.
- valid
Testing dataset with column Class ('Case' vs. 'Control') and features starting with 'hsa'.
- eta
Bonderies of 'eta' parameter in XGBoost training, must be a vector of 2.
- gamma
Bonderies of 'gamma' parameter in XGBoost training, must be a vector of 2.
- max_depth
Bonderies of 'max_depth' parameter in XGBoost training, must be a vector of 2.
- min_child_weight
Bonderies of 'min_child_weight' parameter in XGBoost training, must be a vector of 2.
- subsample
Bonderies of 'subsample' parameter in XGBoost training, must be a vector of 2.
- nfold
Bonderies of 'nfold' parameter in XGBoost training, must be a vector of 2.