rand_forest: Random forest

In parsnip: A Common API to Modeling and Analysis Functions

Random forest

Description

rand_forest() defines a model that creates a large number of decision trees, each independent of the others. The final prediction uses all predictions from the individual trees and combines them. This function can fit classification, regression, and censored regression models.

\Sexpr[stage=render,results=rd]{parsnip:::make_engine_list("rand_forest")}

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

Usage

rand_forest(
  mode = "unknown",
  engine = "ranger",
  mtry = NULL,
  trees = 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.
mtry	An integer for the number of predictors that will be randomly sampled at each split when creating the tree models.
trees	An integer for the number of trees contained in the ensemble.
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
rand_forest(argument = !!value)

References

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

See Also

\Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("rand_forest")}

Examples

show_engines("rand_forest")

rand_forest(mode = "classification", trees = 2000)

parsnip documentation built on June 24, 2024, 5:14 p.m.