details_bag_mars_earth: Bagged MARS via earth

In parsnip: A Common API to Modeling and Analysis Functions

Bagged MARS via earth

Description

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.

parsnip documentation built on June 24, 2024, 5:14 p.m.