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)
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
#>