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.
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()
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>