boost_tree | R Documentation |

`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.

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
)
```

`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` |
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 |

`stop_iter` |
The number of iterations without improvement before stopping (specific engines only). |

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)

https://www.tidymodels.org, *Tidy Modeling with R*, searchable table of parsnip models

,
`xgb_train()`

, `C5.0_train()`

```
show_engines("boost_tree")
boost_tree(mode = "classification", trees = 20)
```

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