`baguette::bagger()`

creates an collection of decision trees forming an
ensemble. All trees in the ensemble are combined to produce a final prediction.

## Details

For this engine, there is a single mode: classification

### Tuning Parameters

This model has 1 tuning parameters:

`min_n`

: Minimal Node Size (type: integer, default: 2L)

### Translation from parsnip to the original package (classification)

The **baguette** extension package is required to fit this model.

```
library(baguette)
bag_tree(min_n = integer()) %>%
set_engine("C5.0") %>%
set_mode("classification") %>%
translate()
```

```
## Bagged Decision Tree Model Specification (classification)
##
## Main Arguments:
## cost_complexity = 0
## min_n = integer()
##
## Computational engine: C5.0
##
## Model fit template:
## baguette::bagger(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## minCases = integer(), base_model = "C5.0")
```

### Preprocessing requirements

This engine does not require any special encoding of the predictors.
Categorical predictors can be partitioned into groups of factor levels
(e.g. `{a, c}`

vs `{b, d}`

) when splitting at a node. Dummy variables
are not required for this model.

### Case weights

This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on `tidymodels.org`

.

The `fit()`

and `fit_xy()`

arguments have arguments called
`case_weights`

that expect vectors of case weights.