gen_additive_mod() defines a model that can use smoothed functions of
numeric predictors in a generalized linear model.
There are different ways to fit this model. See the engine-specific pages for more details
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
gen_additive_mod( mode = "unknown", select_features = NULL, adjust_deg_free = NULL, engine = "mgcv" )
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".
A single character string specifying what computational engine to use for fitting.
This function only defines what type of model is being fit. Once an engine is specified, the method to fit the model is also defined.
The model is not trained or fit until the
fit.model_spec() function is used
with the data.
show_engines("gen_additive_mod")#> # A tibble: 2 × 2 #> engine mode #> <chr> <chr> #> 1 mgcv regression #> 2 mgcv classificationgen_additive_mod()#> GAM Specification (unknown) #> #> Computational engine: mgcv #>