Generate Model Specification calls to parsnip — fast

Creates a list/tibble of parsnip model specifications.

Usage

fast_regression(
  .data,
  .rec_obj,
  .parsnip_fns = "all",
  .parsnip_eng = "all",
  .split_type = "initial_split",
  .split_args = NULL,
  .drop_na = TRUE
)

Arguments

.data: The data being passed to the function for the regression problem
.rec_obj: The recipe object being passed.
.parsnip_fns: The default is 'all' which will create all possible regression model specifications supported.
.parsnip_eng: the default is 'all' which will create all possible regression model specifications supported.
.split_type: The default is 'initial_split', you can pass any type of split supported by rsample
.split_args: The default is NULL, when NULL then the default parameters of the split type will be executed for the rsample split type.
.drop_na: The default is TRUE, which will drop all NA's from the data.

Value

A list or a tibble.

Details

With this function you can generate a tibble output of any regression model specification and it's fitted workflow object.

Author

Steven P. Sanderson II, MPH

Examples

library(recipes, quietly = TRUE)

rec_obj <- recipe(mpg ~ ., data = mtcars)
frt_tbl <- fast_regression(
  mtcars,
  rec_obj,
  .parsnip_eng = c("lm","glm","gee"),
  .parsnip_fns = "linear_reg"
  )
#> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
#> running glm to get initial regression estimate

frt_tbl
#> # A tibble: 3 × 8
#>   .model_id .parsnip_engine .parsnip_mode .parsnip_fns model_spec wflw      
#>       <int> <chr>           <chr>         <chr>        <list>     <list>    
#> 1         1 lm              regression    linear_reg   <spec[+]>  <workflow>
#> 2         2 gee             regression    linear_reg   <spec[+]>  <workflow>
#> 3         3 glm             regression    linear_reg   <spec[+]>  <workflow>
#> # ℹ 2 more variables: fitted_wflw <list>, pred_wflw <list>

Generate Model Specification calls to `parsnip`