discrim_flexible: Flexible discriminant analysis

View source: R/discrim_flexible.R

discrim_flexibleR Documentation

Flexible discriminant analysis


discrim_flexible() defines a model that fits a discriminant analysis model that can use nonlinear features created using multivariate adaptive regression splines (MARS). This function can fit classification models.


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


  mode = "classification",
  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.

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
discrim_flexible(argument = !!value)


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

See Also


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