Boosted trees via xgboost

xgb_train is a wrapper for xgboost tree-based models where all of the model arguments are in the main function.

xgb_train(
  x,
  y,
  max_depth = 6,
  nrounds = 15,
  eta = 0.3,
  colsample_bytree = 1,
  min_child_weight = 1,
  gamma = 0,
  subsample = 1,
  validation = 0,
  early_stop = NULL,
  objective = NULL,
  ...
)

Arguments

x	A data frame or matrix of predictors
y	A vector (factor or numeric) or matrix (numeric) of outcome data.
max_depth	An integer for the maximum depth of the tree.
nrounds	An integer for the number of boosting iterations.
eta	A numeric value between zero and one to control the learning rate.
colsample_bytree	Subsampling proportion of columns.
min_child_weight	A numeric value for the minimum sum of instance weights needed in a child to continue to split.
gamma	A number for the minimum loss reduction required to make a further partition on a leaf node of the tree
subsample	Subsampling proportion of rows.
validation	A positive number. If on [0, 1) the value, validation is a random proportion of data in x and y that are used for performance assessment and potential early stopping. If 1 or greater, it is the number of training set samples use for these purposes.
early_stop	An integer or NULL. If not NULL, it is the number of training iterations without improvement before stopping. If validation is used, performance is base on the validation set; otherwise, the training set is used.
objective	A single string (or NULL) that defines the loss function that xgboost uses to create trees. See xgboost::xgb.train() for options. If left NULL, an appropriate loss function is chosen.
...	Other options to pass to xgb.train.

Value

A fitted xgboost object.