should I apply recipe to new data in a tidymodel?

I have not clearly understood the tidymodel workflow as it seems on my results.

I have trained a successful xgboost model for binary classification. But when I call predict function on new data, an error arised asking for the target variable (disease, not disease)

predict(disease_wf_model, new_incoming_data[1,] )
Error: Can't subset columns that don't exist.
x Column `disease` doesn't exist.

I suppose the new data has no such variable, so i am asking:

  1. should I execute prep() when defining the recipe or not? (some examples do, some other not)

  2. should I execute the recipe on the new data to predict?

Thanks in advance

It would help a lot to see all of the code.

The object has "wf" in it. Is it a workflow object?

1 Like

Found the issue. The recipe had a string2factor step for target column so when applying it to new data, the target variable was not found.

Something like this:

recipe <- recipes::recipe(disease ~ ., data = train) %>%
       recipes::step_naomit(everything(), skip = TRUE) %>% 
       recipes::step_string2factor(disease) %>%  

disease_wf_model <- my_fit$.workflow[[1]]

I assumed the recipe was smart enough to detect when new data is being used for predictions. My bad.

I had not too much success on finding documentation on moving a tidymodel to production . I will keep searching.

Thanks Max

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