`bag_tree()`

defines an ensemble of decision trees. 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/.

## Usage

```
bag_tree(
mode = "unknown",
cost_complexity = 0,
tree_depth = NULL,
min_n = 2,
class_cost = NULL,
engine = "rpart"
)
```

## Arguments

- mode
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".

- cost_complexity
A positive number for the the cost/complexity parameter (a.k.a.

`Cp`

) used by CART models (specific engines only).- 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.

- class_cost
A non-negative scalar for a class cost (where a cost of 1 means no extra cost). This is useful for when the first level of the outcome factor is the minority class. If this is not the case, values between zero and one can be used to bias to the second level of the factor.

- engine
A single character string specifying what computational engine to use for fitting.

## 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
bag_tree(argument = !!value)
```