Is it possible to calculate prediction intervals from a
tidymodels stacked model?
data("tree_frogs") tree_frogs <- tree_frogs %>% filter(!is.na(latency)) %>% select(-c(clutch, hatched)) set.seed(1) tree_frogs_split <- initial_split(tree_frogs) tree_frogs_train <- training(tree_frogs_split) tree_frogs_test <- testing(tree_frogs_split)
I tried to run something like this:
pi <- predict(tree_frogs_model_st, tree_frogs_test, type = "pred_int")
but this gives an error:
Error in UseMethod("stack_predict") : no applicable method for 'stack_predict' applied to an object of class "NULL"
Reading the documentation of
stacks() I also tried passing "pred_int" in the
pi <- predict(tree_frogs_model_st, tree_frogs_test, opts = list(type = "pred_int"))
but this just gives:
opts is only used with type = raw and was ignored.
For reference, I am trying to do a similar thing that is done in Ch.19 of Tidy Modeling with R book
lm_fit <- fit(lm_wflow, data = Chicago_train) predict(lm_fit, Chicago_test, type = "pred_int")
which seems to work fine for a single model fit like
lm_fit, but apparently not for a stacked model?
Am I missing something? Is it not possible to calculate prediction intervals for stacked models for some reason?