bag_mars() defines an ensemble of generalized linear models that use
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.
earth¹²
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
Usage
bag_mars(
mode = "unknown",
num_terms = NULL,
prod_degree = NULL,
prune_method = NULL,
engine = "earth"
)Arguments
- mode
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".
- 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.
- engine
A single character string specifying what computational engine to use for fitting.
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
bag_mars(argument = !!value)