V-Fold Cross-Validation — vfold

V-fold cross-validation randomly splits the data into V groups of roughly equal size (called "folds"). A resample of the analysis data consisted of V-1 of the folds while the assessment set contains the final fold. In basic V-fold cross-validation (i.e. no repeats), the number of resamples is equal to V.

vfold_cv(data, v = 10, repeats = 1, strata = NULL, breaks = 4, ...)

Arguments

data	A data frame.
v	The number of partitions of the data set.
repeats	The number of times to repeat the V-fold partitioning.
strata	A variable that is used to conduct stratified sampling to create the folds. This could be a single character value or a variable name that corresponds to a variable that exists in the data frame.
breaks	A single number giving the number of bins desired to stratify a numeric stratification variable.
...	Not currently used.

Value

A tibble with classes vfold_cv, rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and one or more identification variables. For a single repeat, there will be one column called id that has a character string with the fold identifier. For repeats, id is the repeat number and an additional column called id2 that contains the fold information (within repeat).

Details

The strata argument causes the random sampling to be conducted within the stratification variable. This can help ensure that the number of data points in the analysis data is equivalent to the proportions in the original data set. (Strata below 10% of the total are pooled together.) When more than one repeat is requested, the basic V-fold cross-validation is conducted each time. For example, if three repeats are used with v = 10, there are a total of 30 splits which as three groups of 10 that are generated separately.

Examples

vfold_cv(mtcars, v = 10)
#> #  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
vfold_cv(mtcars, v = 10, repeats = 2)
#> #  10-fold cross-validation repeated 2 times 
#> # A tibble: 20 x 3
#>    splits         id      id2   
#>    <list>         <chr>   <chr> 
#>  1 <split [28/4]> Repeat1 Fold01
#>  2 <split [28/4]> Repeat1 Fold02
#>  3 <split [29/3]> Repeat1 Fold03
#>  4 <split [29/3]> Repeat1 Fold04
#>  5 <split [29/3]> Repeat1 Fold05
#>  6 <split [29/3]> Repeat1 Fold06
#>  7 <split [29/3]> Repeat1 Fold07
#>  8 <split [29/3]> Repeat1 Fold08
#>  9 <split [29/3]> Repeat1 Fold09
#> 10 <split [29/3]> Repeat1 Fold10
#> 11 <split [28/4]> Repeat2 Fold01
#> 12 <split [28/4]> Repeat2 Fold02
#> 13 <split [29/3]> Repeat2 Fold03
#> 14 <split [29/3]> Repeat2 Fold04
#> 15 <split [29/3]> Repeat2 Fold05
#> 16 <split [29/3]> Repeat2 Fold06
#> 17 <split [29/3]> Repeat2 Fold07
#> 18 <split [29/3]> Repeat2 Fold08
#> 19 <split [29/3]> Repeat2 Fold09
#> 20 <split [29/3]> Repeat2 Fold10

library(purrr)
data(wa_churn, package = "modeldata")

set.seed(13)
folds1 <- vfold_cv(wa_churn, v = 5)
map_dbl(folds1$splits,
        function(x) {
          dat <- as.data.frame(x)$churn
          mean(dat == "Yes")
        })
#> [1] 0.2649982 0.2660632 0.2609159 0.2679681 0.2669033

set.seed(13)
folds2 <- vfold_cv(wa_churn, strata = "churn", v = 5)
map_dbl(folds2$splits,
        function(x) {
          dat <- as.data.frame(x)$churn
          mean(dat == "Yes")
        })
#> [1] 0.2653532 0.2653532 0.2653532 0.2653532 0.2654365

set.seed(13)
folds3 <- vfold_cv(wa_churn, strata = "tenure", breaks = 6, v = 5)
map_dbl(folds3$splits,
        function(x) {
          dat <- as.data.frame(x)$churn
          mean(dat == "Yes")
        })
#> [1] 0.2656250 0.2661104 0.2652228 0.2638396 0.2660518