xgboost_impl: Wrapper for parsnip::xgb_train

In modeltime: The Tidymodels Extension for Time Series Modeling

Wrapper for parsnip::xgb_train

Description

Wrapper for parsnip::xgb_train

Usage

xgboost_impl(
  x,
  y,
  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,
  objective = NULL,
  counts = TRUE,
  event_level = c("first", "second"),
  ...
)

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	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.
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().

modeltime documentation built on Sept. 2, 2023, 5:06 p.m.