baguette::bagger()
creates an collection of MARS models forming an
ensemble. All models in the ensemble are combined to produce a final prediction.
Details
For this engine, there are multiple modes: classification and regression
Tuning Parameters
This model has 3 tuning parameters:
prod_degree
: Degree of Interaction (type: integer, default: 1L)prune_method
: Pruning Method (type: character, default: ‘backward’)num_terms
: # Model Terms (type: integer, default: see below)
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)
The baguette extension package is required to fit this model.
bag_mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) %>%
set_engine("earth") %>%
set_mode("regression") %>%
translate()
## Bagged MARS Model Specification (regression)
##
## Main Arguments:
## num_terms = integer(1)
## prod_degree = integer(1)
## prune_method = character(1)
##
## Computational engine: earth
##
## Model fit template:
## baguette::bagger(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg(), nprune = integer(1), degree = integer(1),
## pmethod = character(1), base_model = "MARS")
Translation from parsnip to the original package (classification)
The baguette extension package is required to fit this model.
library(baguette)
bag_mars(
num_terms = integer(1),
prod_degree = integer(1),
prune_method = character(1)
) %>%
set_engine("earth") %>%
set_mode("classification") %>%
translate()
## Bagged MARS Model Specification (classification)
##
## Main Arguments:
## num_terms = integer(1)
## prod_degree = integer(1)
## prune_method = character(1)
##
## Computational engine: earth
##
## Model fit template:
## baguette::bagger(formula = missing_arg(), data = missing_arg(),
## weights = missing_arg(), nprune = integer(1), degree = integer(1),
## pmethod = character(1), base_model = "MARS")
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.”
References
Breiman, L. 1996. “Bagging predictors”. Machine Learning. 24 (2): 123-140
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.