`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.

`mgcv¹`

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

## Usage

```
gen_additive_mod(
mode = "unknown",
select_features = NULL,
adjust_deg_free = NULL,
engine = "mgcv"
)
```

## Arguments

- mode
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".

- select_features
`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.- adjust_deg_free
If

`select_features = TRUE`

, then acts as a multiplier for smoothness. Increase this beyond 1 to produce smoother models.- engine
A single character string specifying what computational engine to use for fitting.

## Details

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)
```

## Examples

```
show_engines("gen_additive_mod")
#> # A tibble: 2 × 2
#> engine mode
#> <chr> <chr>
#> 1 mgcv regression
#> 2 mgcv classification
gen_additive_mod()
#> GAM Model Specification (unknown mode)
#>
#> Computational engine: mgcv
#>
```