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)

Arguments

x

A recipe, parsnip model specification, 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

# \donttest{ 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 x 5 #> K weight_func dist_power lon lat #> <int> <chr> <dbl> <int> <int> #> 1 33 triweight 0.325 10 3
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 = 0.324624970788136 #> #> Computational engine: kknn #>
# }