tests/testthat/_snaps/predict_formats.md

In tidymodels/parsnip: A Common API to Modeling and Analysis Functions

predict(type = "prob") with level "class" (see #720)

Code
  predict(mod, type = "prob", new_data = x)
Condition
  Error in `check_spec_levels()`:
  ! The outcome variable `boop` has a level called "class".
  i This value is reserved for parsnip's classification internals; please change the levels, perhaps with `forcats::fct_relevel()`.

non-factor classification

Code
  logistic_reg() %>% set_engine("glm") %>% fit(class ~ ., data = hpc %>% dplyr::mutate(
    class = class == "VF"))
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a <factor>, not a logical vector.

Code
  logistic_reg() %>% set_engine("glm") %>% fit(class ~ ., data = hpc %>% dplyr::mutate(
    class = ifelse(class == "VF", 1, 0)))
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a <factor>, not a double vector.

Code
  multinom_reg() %>% set_engine("glmnet") %>% fit(class ~ ., data = hpc %>%
    dplyr::mutate(class = as.character(class)))
Condition
  Error in `check_outcome()`:
  ! For a classification model, the outcome should be a <factor>, not a character vector.

tidymodels/parsnip documentation built on Feb. 19, 2025, 2:10 a.m.