upsample.md
In themis: Extra Recipes Steps for Dealing with Unbalanced Data

ratio deprecation

Code
  new_rec <- recipe(~., data = circle_example) %>% step_upsample(class, ratio = 2)
Error <lifecycle_error_deprecated>
  The `ratio` argument of `step_downsample()` was deprecated in themis 0.2.0 and is now defunct.
  i Please use the `over_ratio` argument instead.

bad data

Code
  rec %>% step_upsample(x) %>% prep()
Error <recipes_error_step>
  Error in `step_upsample()`:
  Caused by error in `prep()`:
  ! `x` should be a factor variable.

Code
  rec %>% step_upsample(class, id) %>% prep()
Error <recipes_error_step>
  Error in `step_upsample()`:
  Caused by error in `prep()`:
  ! The selector should select at most a single variable

case_weights

Code
  rec1_p
Message <cliMessage>

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

  -- Inputs 
  Number of variables by role
  predictor:    4
  case_weights: 1

  -- Training information 
  Training data contained 400 data points and no incomplete rows.

  -- Operations 
  * Up-sampling based on: class | Trained, weighted

Code
  rec1_p
Message <cliMessage>

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

  -- Inputs 
  Number of variables by role
  predictor:    4
  case_weights: 1

  -- Training information 
  Training data contained 400 data points and no incomplete rows.

  -- Operations 
  * Up-sampling based on: class | Trained, ignored weights

empty printing

Code
  rec
Message <cliMessage>

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

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

  -- Operations 
  * Up-sampling based on: <none>

Code
  rec
Message <cliMessage>

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

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

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

  -- Operations 
  * Up-sampling based on: <none> | Trained

printing

Code
  print(rec)
Message <cliMessage>

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

  -- Inputs 
  Number of variables by role
  predictor: 4

  -- Operations 
  * Up-sampling based on: class

Code
  prep(rec)
Message <cliMessage>

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

  -- Inputs 
  Number of variables by role
  predictor: 4

  -- Training information 
  Training data contained 400 data points and no incomplete rows.

  -- Operations 
  * Up-sampling based on: class | Trained