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

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)

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



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parsnip documentation built on Aug. 18, 2023, 1:07 a.m.