The finalize_* functions take a list or tibble of tuning parameter values and
update objects with those values.
finalize_model(x, parameters)
finalize_recipe(x, parameters)
finalize_workflow(x, parameters)
finalize_tailor(x, parameters)A recipe, parsnip model specification, tailor postprocessor, or workflow.
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".
An updated version of x.
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
#>