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.
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
decision_tree( 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
The model is not trained or fit until the
fit() function is used
with the data.
value <- 1 decision_tree(argument = !!value)
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 #>