use_xgboost() uses only 6/8 possible tuning parameters

[dpprdan]

use_xgboost() only uses 6 of the 8 possible tuning parameters (i.e. mtry and stop_iter are not tune()d).
Is that a deliberate choice (if so, could/should be documented?) or an oversight?
Or am I just missing something?

library(usemodels)
library(tidymodels, warn.conflicts = FALSE)
data(ames)

ames <-
  ames |>
  select(
    Sale_Price,
    Neighborhood,
    Gr_Liv_Area,
    Year_Built,
    Bldg_Type,
    Latitude,
    Longitude
  ) |> 
  mutate(Sale_Price = log10(Sale_Price))

ames_split <- initial_split(ames, prop = 0.80)
ames_train <- training(ames_split)
ames_test  <- testing(ames_split)

use_xgboost(
  Sale_Price ~ Neighborhood + Gr_Liv_Area + Year_Built + Bldg_Type + Latitude + Longitude, 
  data = ames_train
)
#> xgboost_recipe <- 
#>   recipe(formula = Sale_Price ~ Neighborhood + Gr_Liv_Area + Year_Built + Bldg_Type + 
#>     Latitude + Longitude, data = ames_train) %>% 
#>   step_novel(all_nominal_predictors()) %>% 
#>   step_dummy(all_nominal_predictors(), one_hot = TRUE) %>% 
#>   step_zv(all_predictors()) 
#> 
#> xgboost_spec <- 
#>   boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), 
#>     loss_reduction = tune(), sample_size = tune()) %>% 
#>   set_mode("regression") %>% 
#>   set_engine("xgboost") 
#> 
#> xgboost_workflow <- 
#>   workflow() %>% 
#>   add_recipe(xgboost_recipe) %>% 
#>   add_model(xgboost_spec) 
#> 
#> set.seed(8291)
#> xgboost_tune <-
#>   tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))

Session info

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[topepo]

Sort of deliberate. mtry requires the user to know the number of predictors (which might not be obvious). We can't make a default for that so it was left out.

For stop_iter, I view that as an alternate method for tuning. I would only tune that or trees but not both.

[jrosell]

One alternative is to use count = TRUE as engine argument and use mtry as a proportion. Would that work?