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. This function can
fit classification and regression models.
There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.
¹ The default engine.More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
Usage
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".
- engine
A single character string specifying what computational engine to use for fitting.
- num_terms
The number of features that will be retained in the final model, including the intercept.
- prod_degree
The highest possible interaction degree.
- prune_method
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. 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
mars(argument = !!value)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
#>