sample.md
In recipes: Preprocessing and Feature Engineering Steps for Modeling

bad input

Code
  prep(step_sample(iris_rec, size = -1))
Condition
  Error in `step_sample()`:
  Caused by error in `prep()`:
  ! `size` must be a number larger than or equal to 0 or `NULL`, not the number -1.

Code
  prep(step_sample(iris_rec, size = "a"))
Condition
  Error in `step_sample()`:
  Caused by error in `prep()`:
  ! `size` must be a number or `NULL`, not the string "a".

Code
  prep(step_sample(iris_rec, replace = "a"))
Condition
  Error in `step_sample()`:
  Caused by error in `prep()`:
  ! `replace` must be `TRUE` or `FALSE`, not the string "a".

sample with case weights

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  predictor:    10
  case_weights:  1

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
  * Row sampling: <none> | Trained, weighted

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  predictor:    10
  case_weights:  1

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
  * Row sampling: <none> | Trained, weighted

warn when selectors are provided

Code
  tmp <- step_sample(recipe(~., data = mtcars), all_predictors())
Condition
  Warning:
  Selectors are not used for this step.

empty printing

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Operations 
  * Row sampling: <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 
  * Row sampling: <none> | Trained

printing

Code
  print(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  predictor: 5

  -- Operations 
  * Row sampling: <none>

Code
  prep(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  predictor: 5

  -- Training information 
  Training data contained 150 data points and no incomplete rows.

  -- Operations 
  * Row sampling: <none> | Trained