`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.

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/.

## 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. 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.

## Examples

```
show_engines("rand_forest")
#> # A tibble: 6 × 2
#> engine mode
#> <chr> <chr>
#> 1 ranger classification
#> 2 ranger regression
#> 3 randomForest classification
#> 4 randomForest regression
#> 5 spark classification
#> 6 spark regression
rand_forest(mode = "classification", trees = 2000)
#> Random Forest Model Specification (classification)
#>
#> Main Arguments:
#> trees = 2000
#>
#> Computational engine: ranger
#>
```