`boost_tree()`

defines a model that creates a series of decision trees
forming an ensemble. Each tree depends on the results of previous trees.
All trees in the ensemble are combined to produce a final prediction. 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

```
boost_tree(
mode = "unknown",
engine = "xgboost",
mtry = NULL,
trees = NULL,
min_n = NULL,
tree_depth = NULL,
learn_rate = NULL,
loss_reduction = NULL,
sample_size = NULL,
stop_iter = 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
A number for the number (or proportion) of predictors that will be randomly sampled at each split when creating the tree models (specific engines only).

- 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 is required for the node to be split further.

- tree_depth
An integer for the maximum depth of the tree (i.e. number of splits) (specific engines only).

- learn_rate
A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (specific engines only). This is sometimes referred to as the shrinkage parameter.

- loss_reduction
A number for the reduction in the loss function required to split further (specific engines only).

- sample_size
A number for the number (or proportion) of data that is exposed to the fitting routine. For

`xgboost`

, the sampling is done at each iteration while`C5.0`

samples once during training.- stop_iter
The number of iterations without improvement before stopping (specific engines only).

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

## Examples

```
show_engines("boost_tree")
#> # A tibble: 5 × 2
#> engine mode
#> <chr> <chr>
#> 1 xgboost classification
#> 2 xgboost regression
#> 3 C5.0 classification
#> 4 spark classification
#> 5 spark regression
boost_tree(mode = "classification", trees = 20)
#> Boosted Tree Model Specification (classification)
#>
#> Main Arguments:
#> trees = 20
#>
#> Computational engine: xgboost
#>
```