Trim all unnecessary data from model objects

trim_model.R

library(future)
trim_model <- function(model, predictor = predict, ..., ignore_warnings = TRUE) {
  # Cache the correct output
  true_pred <- predictor(model, ...)
  
  # Treat prediction warnings as errors?
  if (!ignore_warnings) {
    old_ops <- options(warn = 2)
    on.exit(options(old_ops))
  }
  
  # Define Recursion function
  trimmer <- function(model, path = integer()) {
    if (length(path) != 0) { # Not the top level object
      # Try a model without this element
      temp_model <- model
      temp_model[[path]] <- NULL
      # Run in another process to avoid crashing main session
      try_pred <- multicore(predictor(temp_model, ...))
      # No change if removed - set to NULL
      if (identical(try(value(try_pred), silent = TRUE), true_pred)) return(NULL)
      # Change if removed - dig into it
      element <- model[[path]]
    } else { # Top level model object - dig into it
      element <- model
    }
    # Only dig into list elements, ignoring data.frames
    if (!(is.list(element) && !is.data.frame(element))) return(element)
    # Try every subelement
    element_class <- class(element)
    element <- unclass(element)
    for (i in rev(seq_along(element))) { # Loop backwards to avoid changing index
      if (is.null(element[[i]])) next
      element[[i]] <- trimmer(model, c(path, i)) # Trim element
      # Update model to match trimmed element
      if (length(path) == 0) model <- structure(element, class = element_class)
      else model[[path]] <- structure(element, class = element_class)
    }
    # Reclass the list
    class(element) <- element_class
    element
  }
  
  # Trim
  trimmer(model)
}

Does this work on train objects from the caret package?