View source: R/decision_tree.R
decision_tree | R Documentation |
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 https://www.tidymodels.org/.
decision_tree( mode = "unknown", engine = "rpart", cost_complexity = NULL, tree_depth = NULL, min_n = NULL )
mode |
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification". |
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. |
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. |
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.
https://www.tidymodels.org, Tidy Modeling with R, searchable table of parsnip models
show_engines("decision_tree") decision_tree(mode = "classification", tree_depth = 5)
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