In the recipes package, there are no constraints on the order in which steps are added to the recipe; you as a user are free to apply steps in the order appropriate to your data preprocessing needs. However, the order of steps matters and there are some general suggestions that you should consider.
The order of steps for handling categorical levels is important, because each step sets levels for the next step to use as input. These steps create factor output, even if the input is of character type.
step_novel() before other steps for
changing factor levels, so that the new factor level can be set as you
desire rather than coerced to
NA by other factor handling
step_other() after other steps for changing factor
Recipes do not automatically create dummy variables (unlike most formula methods).
step_dummy() first so
that numeric columns are in the data set instead of factors.
step_interact(), you should make dummy
variables before creating the interactions.
While every individual project’s needs are different, here is a suggested order of potential steps that should work for most problems:
Again, your mileage may vary for your particular problem.