implementing high cardinality categorical variables in R using caret's dummyVars function
I'm currently working on a predictive modeling project in R using the `caret` package (version 6.0-86). I'm working with a scenario where I need to create dummy variables from a high cardinality categorical variable, but the `dummyVars` function doesn't seem to handle it as I expected. The variable in question has over 100 unique categories, and when I run the following code: ```r library(caret) # Sample data set.seed(123) data <- data.frame( id = 1:10, category = sample(paste0('cat', 1:120), 10), value = rnorm(10) ) # Create dummy variables dummy_model <- dummyVars(value ~ category, data = data) dummy_data <- predict(dummy_model, newdata = data) ``` I get the following warning message: ``` Warning: The parameter `dummyVars` is too high, using only the first 100 categories. ``` After running this, I noticed that not all dummy variables are created for the categories, which is problematic because I need to include all categories for my model. I’ve tried setting the `max_levels` argument in `dummyVars`, but it doesn't seem to have any effect. Is there a way to handle this scenario effectively or another approach I should consider using for high cardinality variables? Any best practices for dealing with this would also be greatly appreciated.