rule_fit: RuleFit models

In parsnip: A Common API to Modeling and Analysis Functions

RuleFit models

Description

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.

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

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

Usage

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"
)

Arguments

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 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).
penalty	L1 regularization parameter.
engine	A single character string specifying what computational engine to use for fitting.

Details

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)

References

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

See Also

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

Examples

show_engines("rule_fit")

rule_fit()

parsnip documentation built on June 24, 2024, 5:14 p.m.