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

input checking

Code
  recipe(~., data = df) %>% step_relu(val1, shift = TRUE) %>% prep(df, verbose = FALSE)
Condition
  Error in `step_relu()`:
  ! Shift argument must be a numeric value.

Code
  recipe(~., data = df) %>% step_relu(val1, reverse = 3) %>% prep(df, verbose = FALSE)
Condition
  Error in `step_relu()`:
  ! Reverse argument must be a logical value.

Code
  recipe(~., data = df) %>% step_relu(val1, smooth = "cat") %>% prep(df, verbose = FALSE)
Condition
  Error in `step_relu()`:
  ! Smooth argument must be logical value.

Code
  recipe(~., data = df) %>% step_relu(val2) %>% prep(df, verbose = FALSE)
Condition
  Error in `step_relu()`:
  Caused by error in `prep()`:
  ! All columns selected for the step should be double, or integer.

empty printing

Code
  rec
Message

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

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

  -- Operations 
  * Adding relu transform 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 
  * Adding relu transform for: <none> | Trained

printing

Code
  print(rec)
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 11

  -- Operations 
  * Adding relu transform for: disp

Code
  prep(rec)
Message

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

  -- Inputs 
  Number of variables by role
  predictor: 11

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

  -- Operations 
  * Adding relu transform for: disp | Trained