select hyperparameters using Latin hypercube sampling (LHS) from a large matrix/grid of parameter combinations I have a matrix with each row corresponds to a hyperparameter for the XGBoost model. There are seven parameters to tune in XGBoost (as shown below: nrounds/iterations, max_depth, eta, gamma, colsample_byTree, min_child_weight, and subsample). I did a literature review to specify the range and interval of values for each parameter. Using those ranges and intervals, the parameter space generated around 62,500 parameter combinations. I am using R caret::train function to generate the best hyperparameter combination for my dataset. However, the amount of simulations (62,500) is too much. I read about the Latin hypercube sampling (LHS) and I think that is what I need to reduce the number of simulations by applying initial selection of hyperparameters using LHS. But I am having trouble implementing the approach in my dataset. My goal is to generate a manageable number of hyperparameter combinations (i.e., ~500) using LHS, and then use caret::train function to select best parameters. I would like to ask for help in implementing LHS using my parameter space.
nrounds &lt;- seq(from = 200, to = 1000, by = 200) 
maxdepth &lt;- seq(from = 2, to = 10, by = 2)
eta &lt;- c(0.01, 0.05, 0.1, 0.2, 0.3)
gamma &lt;- seq(from = 0, to = 0.4, by = 0.1)
colsample_bytree &lt;- seq(from = 0.4, to = 1, by = 0.2)
min_child_weight &lt;- seq(from = 1, to = 5, by = 1)
subsample &lt;- seq(from = 0.6, to = 1, by = 0.1)
dataGrid &lt;- expand.grid(nrounds, maxdepth, eta, gamma, colsample_bytree, min_child_weight, subsample)
dials from Tidymodels has a grid_latin_hypercube function you can use for this https://dials.tidymodels.org/reference/grid_max_entropy.html