OmicSelector_xgboost: OmicSelector_xgboost

In kstawiski/OmicSelector: OmicSelector - a package for biomarker selection based on high-throughput experiments.

OmicSelector_xgboost

Description

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.

Value

Xgboost model

kstawiski/OmicSelector documentation built on April 10, 2024, 11:11 p.m.