I've adapted some code from several public Tidymodels examples to setup an XGBoost model with hyperparameter tuning:

```
# ____________________________________
#Set up the model specification
#The hyperparameters will be tuned
xgb_spec <-
boost_tree(
trees = 1000,
tree_depth = tune(),
min_n = tune(),
loss_reduction = tune(),
sample_size = tune(),
mtry = tune(),
learn_rate = tune()) %>%
set_engine("xgboost") %>%
set_mode("regression")
#____________________________________
#____________________________________
#Set up a space-filling grid design to cover the hyperparameter space as well as possible
xgb_grid <-
grid_latin_hypercube(
tree_depth(),
min_n(),
loss_reduction(),
sample_size = sample_prop(),
finalize(mtry(), training_set), #gets treated differently b/c it depends on actual # of predictors in data
learn_rate(),
size = 40)
#____________________________________
```

I understand that the `grid_latin_hypercube`

here is supposed to use LHS to explore the hyperparameter space efficiently/uniformly, but I was confused to see that `sample_size()`

is sampled over a range of ~0.1 to ~0.99, and `loss_reduction`

from 1.5e-10 to ~2.

When plotting the 6 parameters' values across each of the 40 tested configurations, this is clear:

(ignore model name)

Is this as expected? Is this simply a function of not being able to put different parameters on the same scale, and if so, why are some sampled on different scales?

Thanks!