mars() defines a generalized linear model that uses artificial features for some predictors. These features resemble hinge functions and the result is a model that is a segmented regression in small dimensions.

There are different ways to fit this model. See the engine-specific pages for more details:

• earth (default)

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

mars(
mode = "unknown",
engine = "earth",
num_terms = NULL,
prod_degree = NULL,
prune_method = NULL
)

## Arguments

mode A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification". A single character string specifying what computational engine to use for fitting. The number of features that will be retained in the final model, including the intercept. The highest possible interaction degree. The pruning method.

## Details

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.

## References

fit.model_spec(), set_engine(), update(), earth engine details

## Examples

show_engines("mars")
#> # A tibble: 2 × 2
#>   engine mode
#>   <chr>  <chr>
#> 1 earth  classification
#> 2 earth  regression
mars(mode = "regression", num_terms = 5)
#> MARS Model Specification (regression)
#>
#> Main Arguments:
#>   num_terms = 5
#>
#> Computational engine: earth
#>