mars | R Documentation |
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.
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 )
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. |
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.
https://www.tidymodels.org, Tidy Modeling with R, searchable table of parsnip models
show_engines("mars") mars(mode = "regression", num_terms = 5)
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