`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:

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). |

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.

The model is not trained or fit until the `fit.model_spec()`

function is used
with the data.

https://www.tidymodels.org, *Tidy Models with R*

`fit.model_spec()`

, `set_engine()`

, `update()`

, `xgboost engine details`

, `C5.0 engine details`

, `spark engine details`

,
`xgb_train()`

, `C5.0_train()`

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