Fit a workflow with specific parameters

Usage

fit_results(wf, resamples, param_info = NULL, grid = 10, fn = "tune_grid", ...)

Arguments

wf: workflow
resamples: rset
param_info: for tune_* functions
grid: for tune_* functions
fn: the name of the function to run when tuning
...: Optional engine arguments

Examples

library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.3.0 ──
#> ✔ broom        1.0.8     ✔ recipes      1.3.1
#> ✔ dials        1.4.0     ✔ rsample      1.3.0
#> ✔ dplyr        1.1.4     ✔ tibble       3.3.0
#> ✔ ggplot2      3.5.2     ✔ tidyr        1.3.1
#> ✔ infer        1.0.9     ✔ tune         1.3.0
#> ✔ modeldata    1.4.0     ✔ workflows    1.2.0
#> ✔ parsnip      1.3.2     ✔ workflowsets 1.1.1
#> ✔ purrr        1.0.4     ✔ yardstick    1.3.2
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()
library(xgboost)
#> 
#> Attaching package: ‘xgboost’
#> The following object is masked from ‘package:dplyr’:
#> 
#>     slice
library(modeldata)
data(cells)
split <- cells |>
  mutate(across(where(is.character), as.factor)) |>
  sample_n(500) |>
  initial_split(strata = case)
train <- training(split)
resamples <- vfold_cv(train, v = 2, strata = case)
wf_spec <- train |>
  recipe(case ~ .) |>
  step_integer(all_nominal_predictors()) |>
  workflow(boost_tree(mode = "classification"))
res_spec <- wf_spec |> fit_results(resamples)
res_spec |> collect_metrics()
#> # A tibble: 3 × 6
#>   .metric     .estimator  mean     n std_err .config             
#>   <chr>       <chr>      <dbl> <int>   <dbl> <chr>               
#> 1 accuracy    binary     0.489     2 0.00802 Preprocessor1_Model1
#> 2 brier_class binary     0.317     2 0.00615 Preprocessor1_Model1
#> 3 roc_auc     binary     0.481     2 0.00338 Preprocessor1_Model1