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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
)

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.

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.

See also

Other Model_Generator: create_model_spec(), fast_classification()

Author

Steven P. Sanderson II, MPH

Examples

library(recipes, quietly = TRUE)
library(dplyr, quietly = TRUE)

rec_obj <- recipe(mpg ~ ., data = mtcars)
frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"),
.parsnip_fns = "linear_reg")
glimpse(frt_tbl)
#> Rows: 2
#> Columns: 8
#> $ .model_id       <int> 1, 2
#> $ .parsnip_engine <chr> "lm", "glm"
#> $ .parsnip_mode   <chr> "regression", "regression"
#> $ .parsnip_fns    <chr> "linear_reg", "linear_reg"
#> $ model_spec      <list> [~NULL, ~NULL, NULL, regression, TRUE, NULL, lm, TRUE]…
#> $ wflw            <list> [cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb, mp…
#> $ fitted_wflw     <list> [cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb, mp…
#> $ pred_wflw       <list> [<tbl_df[8 x 1]>], [<tbl_df[8 x 1]>]