Multivariate adaptive regression splines (MARS) via earth
Source:R/mars_earth.R
details_mars_earth.Rd
earth::earth()
fits 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.
Details
For this engine, there are multiple modes: classification and regression
Tuning Parameters
This model has 3 tuning parameters:
num_terms
: # Model Terms (type: integer, default: see below)prod_degree
: Degree of Interaction (type: integer, default: 1L)prune_method
: Pruning Method (type: character, default: ‘backward’)
The default value of num_terms
depends on the number of predictor
columns. For a data frame x
, the default is
min(200, max(20, 2 * ncol(x))) + 1
(see
earth::earth()
and the reference below).
Translation from parsnip to the original package (regression)
mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) %>%
set_engine("earth") %>%
set_mode("regression") %>%
translate()
## MARS Model Specification (regression)
##
## Main Arguments:
## num_terms = integer(1)
## prod_degree = integer(1)
## prune_method = character(1)
##
## Computational engine: earth
##
## Model fit template:
## earth::earth(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
## nprune = integer(1), degree = integer(1), pmethod = character(1),
## keepxy = TRUE)
Translation from parsnip to the original package (classification)
mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) %>%
set_engine("earth") %>%
set_mode("classification") %>%
translate()
## MARS Model Specification (classification)
##
## Main Arguments:
## num_terms = integer(1)
## prod_degree = integer(1)
## prune_method = character(1)
##
## Engine-Specific Arguments:
## glm = list(family = stats::binomial)
##
## Computational engine: earth
##
## Model fit template:
## earth::earth(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
## nprune = integer(1), degree = integer(1), pmethod = character(1),
## glm = list(family = stats::binomial), keepxy = TRUE)
An alternate method for using MARs for categorical outcomes can be found
in discrim_flexible()
.
Preprocessing requirements
Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via fit()
, parsnip will
convert factor columns to indicators.
Case weights
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on tidymodels.org
.
The fit()
and fit_xy()
arguments have arguments called
case_weights
that expect vectors of case weights.
Note that the earth
package documentation has: “In the current
implementation, building models with weights can be slow.”
Saving fitted model objects
This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.
Examples
The “Fitting and Predicting with parsnip” article contains
examples
for mars()
with the "earth"
engine.
References
Friedman, J. 1991. “Multivariate Adaptive Regression Splines.” The Annals of Statistics, vol. 19, no. 1, pp. 1-67.
Milborrow, S. “Notes on the earth package.”
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.