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Decision trees

Source: R/decision_tree.R
decision_tree.Rd

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.

There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

¹ The default engine. ² Requires a parsnip extension package for censored regression, classification, and regression.

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

Usage

decision_tree(
  mode = "unknown",
  engine = "rpart",
  cost_complexity = NULL,
  tree_depth = NULL,
  min_n = NULL
)

Arguments

mode

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

engine

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

cost_complexity

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

tree_depth

An integer for maximum depth of the tree.

min_n

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

Details

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

https://www.tidymodels.org, Tidy Modeling with R, searchable table of parsnip models

See also

fit(), set_engine(), update(), rpart engine details, C5.0 engine details, partykit engine details, spark engine details

Examples

show_engines("decision_tree")
#> # A tibble: 5 × 2
#>   engine mode          
#>   <chr>  <chr>         
#> 1 rpart  classification
#> 2 rpart  regression    
#> 3 C5.0   classification
#> 4 spark  classification
#> 5 spark  regression    

decision_tree(mode = "classification", tree_depth = 5)
#> Decision Tree Model Specification (classification)
#> 
#> Main Arguments:
#>   tree_depth = 5
#> 
#> Computational engine: rpart 
#>