bag_mars: Ensembles of MARS models

View source: R/bag_mars.R

bag_marsR Documentation

Ensembles of MARS models


bag_mars() defines an ensemble of generalized linear models that use artificial features for some predictors. These features resemble hinge functions and the result is a model that is a segmented regression in small dimensions. This function can fit classification and regression models.


More information on how parsnip is used for modeling is at


  mode = "unknown",
  num_terms = NULL,
  prod_degree = NULL,
  prune_method = NULL,
  engine = "earth"



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


The number of features that will be retained in the final model, including the intercept.


The highest possible interaction degree.


The pruning method.


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


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.

References, Tidy Modeling with R, searchable table of parsnip models

See Also


parsnip documentation built on March 7, 2023, 5:57 p.m.