Nothing
Code
recipe(~medium, data = tate_text) %>% step_dummy_extract(medium) %>% prep()
Condition
Error in `step_dummy_extract()`:
Caused by error in `prep()`:
! `sep` or `pattern` must be specified.
Code
prep(rec, training = dat)
Condition
Error in `step_dummy_extract()`:
Caused by error in `bake()`:
! Name collision occurred. The following variable names already exist:
* `Species_setosa`
Code
dummy_prepped
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 1
case_weights: 1
-- Training information
Training data contained 4 data points and no incomplete rows.
-- Operations
* Extract patterns from: medium | Trained, weighted
Code
dummy_prepped
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 1
case_weights: 1
-- Training information
Training data contained 4 data points and no incomplete rows.
-- Operations
* Extract patterns from: medium | Trained, ignored weights
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Operations
* Extract patterns from: <none>
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Training information
Training data contained 32 data points and no incomplete rows.
-- Operations
* Extract patterns from: <none> | Trained
Code
rec <- prep(rec)
Condition
Warning:
`keep_original_cols` was added to `step_dummy_extract()` after this recipe was created.
i Regenerate your recipe to avoid this warning.
Code
print(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 1
-- Operations
* Extract patterns from: all_predictors()
Code
prep(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 1
-- Training information
Training data contained 4284 data points and no incomplete rows.
-- Operations
* Extract patterns from: medium | Trained
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