
Internals Safely Make Predictions on a Fitted Workflow from Model Spec tibble
Source:R/internals-make-preds-wflw.R
internal_make_wflw_predictions.Rd
Safely Make predictions on a fitted workflow from a model spec tibble.
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 functioninternal_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.
See also
Other Internals:
internal_make_fitted_wflw()
,
internal_make_spec_tbl()
,
internal_make_wflw()
,
internal_set_args_to_tune()
,
make_classification_base_tbl()
,
make_regression_base_tbl()
Examples
library(recipes, quietly = TRUE)
library(dplyr, quietly = TRUE)
mod_spec_tbl <- fast_regression_parsnip_spec_tbl(
.parsnip_eng = c("lm","glm","gee"),
.parsnip_fns = "linear_reg"
)
rec_obj <- recipe(mpg ~ ., data = mtcars)
splits_obj <- create_splits(mtcars, "initial_split")
mod_tbl <- mod_spec_tbl %>%
mutate(wflw = internal_make_wflw(mod_spec_tbl, rec_obj))
#> Error in `.f()`:
#> ! parsnip could not locate an implementation for `linear_reg` regression
#> model specifications using the `gee` engine.
#> ℹ The parsnip extension package multilevelmod implements support for this
#> specification.
#> ℹ Please install (if needed) and load to continue.
mod_fitted_tbl <- mod_tbl %>%
mutate(fitted_wflw = internal_make_fitted_wflw(mod_tbl, splits_obj))
#> Error in UseMethod("fit"): no applicable method for 'fit' applied to an object of class "NULL"
internal_make_wflw_predictions(mod_fitted_tbl, splits_obj)
#> Error in UseMethod("predict"): no applicable method for 'predict' applied to an object of class "NULL"
#> [[1]]
#> # A tibble: 24 × 1
#> .pred
#> <dbl>
#> 1 21.6
#> 2 21.4
#> 3 10.7
#> 4 32.5
#> 5 18.8
#> 6 20.5
#> 7 11.8
#> 8 17.2
#> 9 15.8
#> 10 27.9
#> # … with 14 more rows
#>
#> [[2]]
#> NULL
#>
#> [[3]]
#> # A tibble: 24 × 1
#> .pred
#> <dbl>
#> 1 21.6
#> 2 21.4
#> 3 10.7
#> 4 32.5
#> 5 18.8
#> 6 20.5
#> 7 11.8
#> 8 17.2
#> 9 15.8
#> 10 27.9
#> # … with 14 more rows
#>