tests/testthat/_snaps/nzv.md

In topepo/recipes: Preprocessing and Feature Engineering Steps for Modeling

altered freq_cut and unique_cut

The `options` argument of `step_nzv()` was deprecated in recipes 0.1.7 and is now defunct.
i Please use the arguments `freq_cut` and `unique_cut` instead.

Deprecation warning

Code
  recipe(~., data = mtcars) %>% step_nzv(options = list(freq_cut = 95 / 5,
  unique_cut = 20))
Condition
  Error:
  ! The `options` argument of `step_nzv()` was deprecated in recipes 0.1.7 and is now defunct.
  i Please use the arguments `freq_cut` and `unique_cut` instead.

nzv with case weights

Code
  recipe(~., dat_caseweights_x2) %>% step_nzv(all_predictors(), freq_cut = exp_freq_cut_int) %>%
    prep()
Message

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

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

  -- Training information 
  Training data contained 50 data points and no incomplete rows.

  -- Operations 
  * Sparse, unbalanced variable filter removed: x4 | Trained, weighted

Code
  recipe(~., dat_caseweights_y) %>% step_nzv(all_predictors(), freq_cut = exp_freq_cut_frag -
    1e-04) %>% prep()
Message

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

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

  -- Training information 
  Training data contained 50 data points and no incomplete rows.

  -- Operations 
  * Sparse, unbalanced variable filter removed: x3 and x4 | Trained, ignored
    weights

empty printing

Code
  rec
Message

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

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

  -- Operations 
  * Sparse, unbalanced variable filter on: <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 
  * Sparse, unbalanced variable filter removed: <none> | Trained

printing

Code
  print(rec)
Message

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

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

  -- Operations 
  * Sparse, unbalanced variable filter on: x1, x2, x3, x4

Code
  prep(rec)
Message

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

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

  -- Training information 
  Training data contained 50 data points and no incomplete rows.

  -- Operations 
  * Sparse, unbalanced variable filter removed: x3 and x4 | Trained

topepo/recipes documentation built on April 10, 2024, 10:30 p.m.