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
orFALSE.
IfTRUE
, the model has the ability to eliminate a predictor (via penalization). Increasingadjust_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
#>