man/rmd/bag_mars_earth.md

In parsnip: A Common API to Modeling and Analysis Functions

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 \code{\link[=fit.model_spec]{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 Aug. 18, 2023, 1:07 a.m.