Internals Safely Make Predictions on a Fitted Workflow from Model Spec tibble

Safely Make predictions on a fitted workflow from a model spec tibble.

Usage

internal_make_wflw_predictions(.model_tbl, .splits_obj)

Arguments

.model_tbl

The model table that is generated from a function like fast_regression_parsnip_spec_tbl(), must have a class of "tidyaml_mod_spec_tbl". This is meant to be used after the function internal_make_fitted_wflw() has been run and the tibble has been saved.

.splits_obj

The splits object from the auto_ml function. It is internal to the auto_ml_ function.

Value

A list object tibble of the outcome variable and it's values along with the testing and training predictions in a single tibble.

.data_category	.data_type	.value
actual	actual	21.0
actual	actual	21.0
actual	actual	22.8
...	...	...
predicted	training	21.0
...	...	...
predicted	training	21.0

Details

Create predictions on a fitted parnsip model from a workflow object.

See also

Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw(), internal_make_wflw_gee_lin_reg(), internal_set_args_to_tune(), make_classification_base_tbl(), make_regression_base_tbl()

Author

Steven P. Sanderson II, MPH

Examples

library(recipes, quietly = TRUE)

mod_spec_tbl <- fast_regression_parsnip_spec_tbl(
  .parsnip_eng = c("lm","glm"),
  .parsnip_fns = "linear_reg"
)

rec_obj <- recipe(mpg ~ ., data = mtcars)
splits_obj <- create_splits(mtcars, "initial_split")

mod_tbl <- mod_spec_tbl |>
  mutate(wflw = full_internal_make_wflw(mod_spec_tbl, rec_obj))

mod_fitted_tbl <- mod_tbl |>
  mutate(fitted_wflw = internal_make_fitted_wflw(mod_tbl, splits_obj))

internal_make_wflw_predictions(mod_fitted_tbl, splits_obj)
#> [[1]]
#> # A tibble: 64 × 3
#>    .data_category .data_type .value
#>    <chr>          <chr>       <dbl>
#>  1 actual         actual       21.4
#>  2 actual         actual       15.2
#>  3 actual         actual       30.4
#>  4 actual         actual       22.8
#>  5 actual         actual       15  
#>  6 actual         actual       14.7
#>  7 actual         actual       32.4
#>  8 actual         actual       19.2
#>  9 actual         actual       21.5
#> 10 actual         actual       18.1
#> # ℹ 54 more rows
#> 
#> [[2]]
#> # A tibble: 64 × 3
#>    .data_category .data_type .value
#>    <chr>          <chr>       <dbl>
#>  1 actual         actual       21.4
#>  2 actual         actual       15.2
#>  3 actual         actual       30.4
#>  4 actual         actual       22.8
#>  5 actual         actual       15  
#>  6 actual         actual       14.7
#>  7 actual         actual       32.4
#>  8 actual         actual       19.2
#>  9 actual         actual       21.5
#> 10 actual         actual       18.1
#> # ℹ 54 more rows
#>