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

r descr_models("gen_additive_mod", "mgcv")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("select_features", "adjust_deg_free"),
                 default = c("FALSE", "1.0"))

param <-
  gen_additive_mod() %>% 
  set_engine("mgcv") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Translation from parsnip to the original package (regression)

gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>% 
  set_engine("mgcv") %>% 
  set_mode("regression") %>% 
  translate()

Translation from parsnip to the original package (classification)

gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>% 
  set_engine("mgcv") %>% 
  set_mode("classification") %>% 
  translate()

Model fitting

This model should be used with a model formula so that smooth terms can be specified. For example:

library(mgcv)

library(mgcv)
gen_additive_mod() %>% 
  set_engine("mgcv") %>% 
  set_mode("regression") %>% 
  fit(mpg ~ wt + gear + cyl + s(disp, k = 10), data = mtcars)

The smoothness of the terms will need to be manually specified (e.g., using s(x, df = 10)) in the formula. Tuning can be accomplished using the adjust_deg_free parameter.

When using a workflow, pass the model formula to [workflows::add_model()]'s formula argument, and a simplified preprocessing formula elsewhere.

spec <- 
  gen_additive_mod() %>% 
  set_engine("mgcv") %>% 
  set_mode("regression")

workflow() %>% 
  add_model(spec, formula = mpg ~ wt + gear + cyl + s(disp, k = 10)) %>% 
  add_formula(mpg ~ wt + gear + cyl + disp) %>% 
  fit(data = mtcars) %>% 
  extract_fit_engine()