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

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r descr_models("mars", "earth")

Tuning Parameters

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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:

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param$item

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

Translation from parsnip to the original package (regression)

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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)

#| label: earth-cls
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

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Case weights

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

Saving fitted model objects

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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.