bag_tree: Ensembles of decision trees

View source: R/bag_tree.R

bag_treeR Documentation

Ensembles of decision trees


bag_tree() defines an ensemble of decision trees. This function can fit classification, regression, and censored regression models.


More information on how parsnip is used for modeling is at


  mode = "unknown",
  cost_complexity = 0,
  tree_depth = NULL,
  min_n = 2,
  class_cost = NULL,
  engine = "rpart"



A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".


A positive number for the the cost/complexity parameter (a.k.a. Cp) used by CART models (specific engines only).


An integer for maximum depth of the tree.


An integer for the minimum number of data points in a node that are required for the node to be split further.


A non-negative scalar for a class cost (where a cost of 1 means no extra cost). This is useful for when the first level of the outcome factor is the minority class. If this is not the case, values between zero and one can be used to bias to the second level of the factor.


A single character string specifying what computational engine to use for fitting.


This function only defines what type of model is being fit. Once an engine is specified, the method to fit the model is also defined. See set_engine() for more on setting the engine, including how to set engine arguments.

The model is not trained or fit until the fit() function is used with the data.

Each of the arguments in this function other than mode and engine are captured as quosures. To pass values programmatically, use the injection operator like so:

value <- 1
bag_tree(argument = !!value)

References, Tidy Modeling with R, searchable table of parsnip models

See Also


parsnip documentation built on Aug. 18, 2023, 1:07 a.m.