rule_fit | R Documentation |

`rule_fit()`

defines a model that derives simple feature rules from a tree
ensemble and uses them as features in a regularized model. This function can
fit classification and regression models.

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

```
rule_fit(
mode = "unknown",
mtry = NULL,
trees = NULL,
min_n = NULL,
tree_depth = NULL,
learn_rate = NULL,
loss_reduction = NULL,
sample_size = NULL,
stop_iter = NULL,
penalty = NULL,
engine = "xrf"
)
```

`mode` |
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification". |

`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). |

`penalty` |
L1 regularization parameter. |

`engine` |
A single character string specifying what computational engine to use for fitting. |

The RuleFit model creates a regression model of rules in two stages. The first stage uses a tree-based model that is used to generate a set of rules that can be filtered, modified, and simplified. These rules are then added as predictors to a regularized generalized linear model that can also conduct feature selection during model training.

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 rule_fit(argument = !!value)

Friedman, J. H., and Popescu, B. E. (2008). "Predictive learning
via rule ensembles." *The Annals of Applied Statistics*, 2(3), 916-954.

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

`xrf::xrf.formula()`

, \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("rule_fit")}

```
show_engines("rule_fit")
rule_fit()
```

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