The finalize_*
functions take a list or tibble of tuning parameter values and
update objects with those values.
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 totune()
. For example, in theExamples
section below, the model hastune("K")
. In this case, the parameter tibble should be "K" and not "neighbors".
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 Preprocessor10_Model1
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
#>