In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("bag_mars", "earth")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("num_terms", "prod_degree", "prune_method"),
                 default = c("see below", "1L", "'backward'"))

param <-
  bag_mars() %>% 
  set_engine("earth") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

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)

r uses_extension("bag_mars", "earth", "regression")

bag_mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) %>% 
  set_engine("earth") %>% 
  set_mode("regression") %>% 
  translate()

Translation from parsnip to the original package (classification)

r uses_extension("bag_mars", "earth", "classification")

library(baguette)

bag_mars(
  num_terms = integer(1),
  prod_degree = integer(1),
  prune_method = character(1)
) %>% 
  set_engine("earth") %>% 
  set_mode("classification") %>% 
  translate()

Preprocessing requirements

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.