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.

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

• xgboost (default)

• C5.0

• spark

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

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", or "classification". A single character string specifying what computational engine to use for fitting. 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) An integer for the number of trees contained in the ensemble. An integer for the minimum number of data points in a node that is required for the node to be split further. An integer for the maximum depth of the tree (i.e. number of splits) (specific engines only). A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (specific engines only). A number for the reduction in the loss function required to split further (specific engines only). 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. 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.

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

## References

fit.model_spec(), set_engine(), update(), xgboost engine details, C5.0 engine details, spark engine details , xgb_train(), C5.0_train()

## 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
#>