Boosted trees via xgboost — xgb

xgb_train() and xgb_predict() are wrappers for xgboost tree-based models where all of the model arguments are in the main function.

Usage

xgb_train(
  x,
  y,
  weights = NULL,
  max_depth = 6,
  nrounds = 15,
  eta = 0.3,
  colsample_bynode = NULL,
  colsample_bytree = NULL,
  min_child_weight = 1,
  gamma = 0,
  subsample = 1,
  validation = 0,
  early_stop = NULL,
  counts = TRUE,
  event_level = c("first", "second"),
  ...
)

xgb_predict(object, new_data, ...)

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_bynode: Subsampling proportion of columns for each node within each tree. See the counts argument below. The default uses all columns.
colsample_bytree: Subsampling proportion of columns for each tree. See the counts argument below. The default uses all 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. By default, all of the training data are used.
validation: The proportion of the data that are used for performance assessment and potential early stopping.
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.
counts: A logical. If FALSE, colsample_bynode and colsample_bytree are both assumed to be proportions of the proportion of columns affects (instead of counts).
event_level: For binary classification, this is a single string of either "first" or "second" to pass along describing which level of the outcome should be considered the "event".
...: Other options to pass to xgb.train() or xgboost's method for predict().
new_data: A rectangular data object, such as a data frame.

Value

A fitted xgboost object.