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