Splice final parameters into objects

The finalize_* functions take a list or tibble of tuning parameter values and update objects with those values.

Usage

finalize_model(x, parameters)

finalize_recipe(x, parameters)

finalize_workflow(x, parameters)

finalize_tailor(x, parameters)

Arguments

x

A recipe, parsnip model specification, tailor postprocessor, or workflow.

parameters

A list or 1-row tibble of parameter values. Note that the column names of the tibble should be the id fields attached to tune(). For example, in the Examples section below, the model has tune("K"). In this case, the parameter tibble should be "K" and not "neighbors".

Value

An updated version of x.

Examples

data("example_ames_knn")

library(parsnip)
knn_model <-
  nearest_neighbor(
    mode = "regression",
    neighbors = tune("K"),
    weight_func = tune(),
    dist_power = tune()
  ) |>
  set_engine("kknn")

lowest_rmse <- select_best(ames_grid_search, metric = "rmse")
lowest_rmse
#> # A tibble: 1 × 6
#>       K weight_func dist_power   lon   lat .config          
#>   <int> <chr>            <dbl> <int> <int> <chr>            
#> 1    33 triweight        0.511    10     3 pre08_mod07_post0

knn_model
#> K-Nearest Neighbor Model Specification (regression)
#> 
#> Main Arguments:
#>   neighbors = tune("K")
#>   weight_func = tune()
#>   dist_power = tune()
#> 
#> Computational engine: kknn 
#> 
finalize_model(knn_model, lowest_rmse)
#> K-Nearest Neighbor Model Specification (regression)
#> 
#> Main Arguments:
#>   neighbors = 33
#>   weight_func = triweight
#>   dist_power = 0.511191629664972
#> 
#> Computational engine: kknn 
#>