decision_tree: Decision trees

View source: R/decision_tree.R

decision_treeR Documentation

Decision trees


decision_tree() defines a model as a set of ⁠if/then⁠ statements that creates a tree-based structure. This function can fit classification, regression, and censored regression models.


More information on how parsnip is used for modeling is at


  mode = "unknown",
  engine = "rpart",
  cost_complexity = NULL,
  tree_depth = NULL,
  min_n = NULL



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


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


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.


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
decision_tree(argument = !!value)

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

See Also




decision_tree(mode = "classification", tree_depth = 5)

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