vfold.md
In rsample: General Resampling Infrastructure

strata

Code
  rs4 <- vfold_cv(mlc_churn, strata = state, pool = 0.01)
Condition
  Warning:
  Stratifying groups that make up 1% of the data may be statistically risky.
  * Consider increasing `pool` to at least 0.1

bad args

`v` must be a single positive integer greater than 1

`v` must be a single positive integer greater than 1

`v` must be a single positive integer greater than 1

The number of rows is less than `v = 500`

Repeated resampling when `v` is 150 would create identical resamples

`repeats` must be a single positive integer

`repeats` must be a single positive integer

printing

Code
  vfold_cv(mtcars)
Output
  #  10-fold cross-validation 
  # A tibble: 10 x 2
     splits         id    
     <list>         <chr> 
   1 <split [28/4]> Fold01
   2 <split [28/4]> Fold02
   3 <split [29/3]> Fold03
   4 <split [29/3]> Fold04
   5 <split [29/3]> Fold05
   6 <split [29/3]> Fold06
   7 <split [29/3]> Fold07
   8 <split [29/3]> Fold08
   9 <split [29/3]> Fold09
  10 <split [29/3]> Fold10

grouping -- bad args

Repeated resampling when `v` is 4 would create identical resamples

Repeated resampling when `v` is `NULL` would create identical resamples

Code
  group_vfold_cv(Orange, v = 1, group = "Tree")
Condition
  Error in `group_vfold_cv()`:
  ! `v` must be a single positive integer greater than 1

grouping -- other balance methods

Code
  rs1
Output
  # Group 5-fold cross-validation 
  # A tibble: 5 x 2
    splits             id       
    <list>             <chr>    
  1 <split [2364/566]> Resample1
  2 <split [2371/559]> Resample2
  3 <split [2360/570]> Resample3
  4 <split [2278/652]> Resample4
  5 <split [2347/583]> Resample5

grouping -- strata

Code
  sizes4
Output
  # A tibble: 5 x 5
    analysis assessment      n     p id       
       <int>      <int>  <int> <int> <chr>    
  1    80004      19996 100000     3 Resample1
  2    79850      20150 100000     3 Resample2
  3    79912      20088 100000     3 Resample3
  4    80131      19869 100000     3 Resample4
  5    80103      19897 100000     3 Resample5

Leaving `v = NULL` while using stratification will set `v` to the number of groups present in the least common stratum.
i Set `v` explicitly to override this warning.

Code
  sizes5
Output
  # A tibble: 5 x 5
    analysis assessment      n     p id       
       <int>      <int>  <int> <int> <chr>    
  1    80096      19904 100000     3 Resample1
  2    79962      20038 100000     3 Resample2
  3    79928      20072 100000     3 Resample3
  4    80058      19942 100000     3 Resample4
  5    79956      20044 100000     3 Resample5

Leaving `v = NULL` while using stratification will set `v` to the number of groups present in the least common stratum.
i Set `v` explicitly to override this warning.

grouping -- printing

Code
  group_vfold_cv(warpbreaks, "tension")
Output
  # Group 3-fold cross-validation 
  # A tibble: 3 x 2
    splits          id       
    <list>          <chr>    
  1 <split [36/18]> Resample1
  2 <split [36/18]> Resample2
  3 <split [36/18]> Resample3

grouping -- printing with ...

Code
  print(group_vfold_cv(warpbreaks, "tension"), n = 2)
Output
  # Group 3-fold cross-validation 
  # A tibble: 3 x 2
    splits          id       
    <list>          <chr>    
  1 <split [36/18]> Resample1
  2 <split [36/18]> Resample2
  # i 1 more row