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.