In recipes: Preprocessing and Feature Engineering Steps for Modeling

knitr::opts_chunk$set(comment = "#>")

Effects of non-standard roles

Recipes can label and retain column(s) of your data set that should not be treated as outcomes or predictors. A unique identifier column or some other ancillary data could be used to troubleshoot issues during model development but may not be either an outcome or predictor.

For example, the modeldata::biomass dataset has a column named sample with information about the specific sample type. We can change that role:

library(recipes)

data(biomass, package = "modeldata")
biomass_train <- biomass[1:100,]
biomass_test <- biomass[101:200,]

rec <- recipe(HHV ~ ., data = biomass_train) %>%
  update_role(sample, new_role = "id variable") %>%
  step_center(carbon)

rec <- prep(rec, biomass_train)

This means that sample is no longer treated as a "predictor" (the default role for columns on the right-hand side of the formula supplied to recipe()) and won't be used in model fitting or analysis, but will still be retained in the data set.

If you really aren't using sample in your recipe, we recommend that you instead remove sample from your dataset before passing it to recipe(). The reason for this is because recipes assumes that all non-standard roles are required at bake() time (or predict() time, if you are using a workflow). Since you didn't use sample in any steps of the recipe, you might think that you don't need to pass it to bake(), but this isn't true because recipes doesn't know that you didn't use it:

biomass_test$sample <- NULL

bake(rec, biomass_test)

As we mentioned before, the best way to avoid this issue is to not even use a role, just remove the sample column from biomass before calling recipe(). In general, predictors and non-standard roles that are supplied to recipe() should be present at both prep() and bake() time.

If you can't remove sample for some reason, then the second best way to get around this issue is to tell recipes that the "id variable" role isn't required at bake() time. You can do that by using update_role_requirements():

rec <- recipe(HHV ~ ., data = biomass_train) %>%
  update_role(sample, new_role = "id variable") %>%
  update_role_requirements("id variable", bake = FALSE) %>%
  step_center(carbon)

rec <- prep(rec, biomass_train)

# No errors!
biomass_test_baked <- bake(rec, biomass_test)