# How to grid search class.weights hyper parameter in Ranger?

I am currently using ranger for binary classification. My dataset is highly imbalanced (10:1). I went over the documentation, and it appeared to me that class.weights could be a good hyperparameter to tune. But I cannot find a proper documentation/examples on how to carry out grid search for this hyperparameter. Can anyone help me with this?

My code looks like the following right now:

# set up the grid for param searching
grid_search <-  expand.grid(
.mtry =  seq(5, 30, by = 10),
.min.node.size = seq(10, 40, by = 15),
.sample.fraction = seq(0.5, 0.9, by = 0.2),
.num.trees = seq(400, 1000, by = 200),
.class.weights = c(0.5, 3), #How to have different weights?
OOB_error = 0
)

for(i in 1:nrow(grid_search)) {
rf_model <- ranger(
formula         = as.factor(trainY$$sale) ~ ., data = trainX, num.trees = grid_search$$.num.trees[i],
mtry            = grid_search$$.mtry[i], min.node.size = grid_search$$.min.node.size[i],
sample.fraction = grid_search$$.sample.fraction[i], probability = TRUE, class.weights = c(0.5, 3), seed = 123 ) # add OOB error to grid grid_search$$OOB_error[i] <- sqrt(rf_model\$prediction.error)
print(grid_search[i,])
}