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)
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)