In tidymodels/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("mars", "earth")

Tuning Parameters

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

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

This model has r nrow(param) tuning parameters:

param$item

Parsnip changes the default range for num_terms to c(50, 500).

Translation from parsnip to the original package (regression)

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)

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

An alternate method for using MARs for categorical outcomes can be found in [discrim_flexible()].

Preprocessing requirements

Case weights

Note that the earth package documentation has: "In the current implementation, building models with weights can be slow."

Saving fitted model objects

Examples

The "Fitting and Predicting with parsnip" article contains examples for mars() with the "earth" engine.

References

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.