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gen_additive_mod() defines a model that can use smoothed functions of numeric predictors in a generalized linear model. This function can fit classification and regression models.

There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

¹ The default engine.

More information on how parsnip is used for modeling is at


  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".


TRUE or FALSE. If TRUE, the model has the ability to eliminate a predictor (via penalization). Increasing adjust_deg_free will increase the likelihood of removing predictors.


If select_features = TRUE, then acts as a multiplier for smoothness. Increase this beyond 1 to produce smoother models.


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. See set_engine() for more on setting the engine, including how to set engine arguments.

The model is not trained or fit until the fit() function is used with the data.

Each of the arguments in this function other than mode and engine are captured as quosures. To pass values programmatically, use the injection operator like so:

value <- 1
gen_additive_mod(argument = !!value)


#> # A tibble: 2 × 2
#>   engine mode          
#>   <chr>  <chr>         
#> 1 mgcv   regression    
#> 2 mgcv   classification

#> GAM Model Specification (unknown mode)
#> Computational engine: mgcv