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

scale by factor of 1 or 2

Code
  not_recommended_standardized_input <- rec %>% step_scale(carbon, id = "scale",
    factor = 3) %>% prep(training = biomass)
Condition
  Warning:
  Scaling `factor` should take either a value of 1 or 2

warns on zv

Code
  prep(rec1)
Condition
  Warning:
  Column(s) have zero variance so scaling cannot be used: `zero_variance`. Consider using `step_zv()` to remove those columns before normalizing
Message

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

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 6

  -- Training information 
  Training data contained 536 data points and no incomplete rows.

  -- Operations 
  * Scaling for: carbon, hydrogen, oxygen, nitrogen, sulfur, ... | Trained

scaling with case weights

Code
  rec
Message

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

  -- Inputs 
  Number of variables by role
  outcome:      1
  predictor:    9
  case_weights: 1

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

  -- Operations 
  * Scaling for: disp, hp, drat, wt, qsec, vs, am, gear, carb | Trained, weighted

Code
  rec
Message

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

  -- Inputs 
  Number of variables by role
  outcome:      1
  predictor:    9
  case_weights: 1

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

  -- Operations 
  * Scaling for: cyl, disp, hp, drat, qsec, vs, am, ... | Trained, ignored
    weights

empty printing

Code
  rec
Message

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

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

  -- Operations 
  * Scaling for: <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 
  * Scaling for: <none> | Trained

printing

Code
  print(rec)
Message

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

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

  -- Operations 
  * Scaling for: disp, wt

Code
  prep(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 
  * Scaling for: disp, wt | Trained