Multivariate adaptive regression splines (MARS)Source:
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.
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 )
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.
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
The model is not trained or fit until the
fit() function is used
with the data.
value <- 1 mars(argument = !!value)
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 #>