rand_forest: Random forest

Description Usage Arguments Details References See Also Examples

View source: R/rand_forest.R

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.

There are different ways to fit this model. See the engine-specific pages for more details:

\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

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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", or "classification".

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.

The model is not trained or fit until the fit.model_spec() function is used with the data.

References

https://www.tidymodels.org, Tidy Models with R

See Also

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

Examples

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show_engines("rand_forest")

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

parsnip documentation built on July 21, 2021, 5:08 p.m.