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parsnip (version 1.1.0)

details_gen_additive_mod_mgcv: Generalized additive models via mgcv

Description

mgcv::gam() fits a generalized linear model with additive smoother terms for continuous predictors.

Arguments

Details

For this engine, there are multiple modes: regression and classification

Tuning Parameters

This model has 2 tuning parameters:

  • select_features: Select Features? (type: logical, default: FALSE)

  • adjust_deg_free: Smoothness Adjustment (type: double, default: 1.0)

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()


## GAM Model Specification (regression)
## 
## Main Arguments:
##   select_features = logical(1)
##   adjust_deg_free = numeric(1)
## 
## Computational engine: mgcv 
## 
## Model fit template:
## mgcv::gam(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
##     select = logical(1), gamma = numeric(1))

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()


## GAM Model Specification (classification)
## 
## Main Arguments:
##   select_features = logical(1)
##   adjust_deg_free = numeric(1)
## 
## Computational engine: mgcv 
## 
## Model fit template:
## mgcv::gam(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
##     select = logical(1), gamma = numeric(1), family = stats::binomial(link = "logit"))

Model fitting

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

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


## parsnip model object
## 
## 
## Family: gaussian 
## Link function: identity 
## 
## Formula:
## mpg ~ wt + gear + cyl + s(disp, k = 10)
## 
## Estimated degrees of freedom:
## 7.52  total = 11.52 
## 
## GCV score: 4.225228

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()


## 
## Family: gaussian 
## Link function: identity 
## 
## Formula:
## mpg ~ wt + gear + cyl + s(disp, k = 10)
## 
## Estimated degrees of freedom:
## 7.52  total = 11.52 
## 
## GCV score: 4.225228

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

Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via fit(), parsnip will convert factor columns to indicators.

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in case_weights and the examples on tidymodels.org.

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of case weights.

Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.

References