r descr_models("mars", "earth")
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).
mars(num_terms = integer(1), prod_degree = integer(1), prune_method = character(1)) %>% set_engine("earth") %>% set_mode("regression") %>% translate()
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()].
Note that the earth
package documentation has: "In the current implementation, building models with weights can be slow."
The "Fitting and Predicting with parsnip" article contains examples for mars()
with the "earth"
engine.
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.
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