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Creates a list/tibble of parsnip model specifications.

Usage

fast_classification(
  .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 classification problem

.rec_obj

The recipe object being passed.

.parsnip_fns

The default is 'all' which will create all possible classification model specifications supported.

.parsnip_eng

the default is 'all' which will create all possible classification 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 classification model specification and it's fitted workflow object. Per recipes documentation explicitly with step_string2factor() it is encouraged to mutate your predictor into a factor before you create your recipe.

See also

Other Model_Generator: create_model_spec(), fast_regression()

Author

Steven P. Sanderson II, MPH

Examples

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

df <- mtcars %>% mutate(cyl = as.factor(cyl))
rec_obj <- recipe(cyl ~ ., data = df)

fct_tbl <- fast_classification(
  .data = df,
  .rec_obj = rec_obj,
  .parsnip_eng = c("glm","LiblineaR"))
#> Warning: There was 1 warning in `dplyr::mutate()`.
#>  In argument: `fitted_wflw = internal_make_fitted_wflw(mod_tbl, splits_obj)`.
#> Caused by warning:
#> ! glm.fit: fitted probabilities numerically 0 or 1 occurred

glimpse(fct_tbl)
#> Rows: 3
#> Columns: 8
#> $ .model_id       <int> 1, 2, 3
#> $ .parsnip_engine <chr> "glm", "LiblineaR", "LiblineaR"
#> $ .parsnip_mode   <chr> "classification", "classification", "classification"
#> $ .parsnip_fns    <chr> "logistic_reg", "logistic_reg", "svm_linear"
#> $ model_spec      <list> [~NULL, ~NULL, NULL, classification, TRUE, NULL, glm, …
#> $ wflw            <list> [mpg, disp, hp, drat, wt, qsec, vs, am, gear, carb, cy…
#> $ fitted_wflw     <list> [mpg, disp, hp, drat, wt, qsec, vs, am, gear, carb, cy…
#> $ pred_wflw       <list> [<tbl_df[24 x 1]>], [<tbl_df[24 x 1]>], [<tbl_df[24 x …