CODEWITHSUNDEEP
rtidymodels
deschen
deschen

Reputation: 10996

Recommended approach to change the engine in a tidymodels workflow

I'm using tidymodels to train and test a Naive Bayes model and predict new data from it.

For training and testing, I've set up the tidymodels workflow as follows:

model_recipe <- recipes::recipe(OUTCOME ~ ., data = dat_train)

model_final <- parsnip::naive_Bayes(Laplace = 1) |>
  parsnip::set_mode("classification") |>
  parsnip::set_engine("klaR", usekernel = FALSE)

model_final_wf <- workflows::workflow() |>
  workflows::add_recipe(model_recipe) |>
  workflows::add_model(model_final)

Now, when I have done my training and testing I want to fit the model, but now do one tiny specification in the set_engine part, i.e. I want to change the priors for each outomce class.

My question is, what is the best way to do this tiny change? Is there an easy way where I can just take my full workflow and update the engine or do I need to re-run the whole engine/workflow part as shown below?

model_final <- parsnip::naive_Bayes(Laplace = 1) |>
  parsnip::set_mode("classification") |>
  parsnip::set_engine("klaR", usekernel = FALSE,
                      prior = rep(0.2, 5))

model_final_wf <- workflows::workflow() |>
  workflows::add_recipe(model_recipe) |>
  workflows::add_model(model_final)

Upvotes: 0

Views: 166

Answers (1)

topepo
topepo

Reputation: 14316

You can just update the engine information (instead of the whole object).

Similarly, you can update just the model in the workflow.

model_recipe <- recipes::recipe(OUTCOME ~ ., data = dat_train)

model_final <- parsnip::naive_Bayes(Laplace = 1) |>
  parsnip::set_mode("classification") |>
  parsnip::set_engine("klaR", usekernel = FALSE)

model_final_wf <- workflows::workflow() |>
  workflows::add_recipe(model_recipe) |>
  workflows::add_model(model_final)

# Update the engine parameters via another `set_engine()`
model_final_final <- 
  model_final %>% 
  set_engine("klaR", usekernel = TRUE)

# Copy the workflow and update with new model spec
model_final_final_wf <- model_final_final_wf |>
 update_model(model_final_final)

Upvotes: 2

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