In 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.

However, when using a workflow, the best approach is to avoid using [workflows::add_formula()] and use [workflows::add_variables()] in conjunction with a model formula:

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

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

The reason for this is that [workflows::add_formula()] will try to create the model matrix and fail to find/use s().

Preprocessing requirements

Case weights

Saving fitted model objects

References

• Ross, W. 2021. Generalized Additive Models in R: A Free, Interactive Course using mgcv

• Wood, S. 2017. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC.

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