V-fold cross-validation (also known as k-fold cross-validation) randomly splits the data into V groups of roughly equal size (called "folds"). A resample of the analysis data consists 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.
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 in
data
(single character or name) used to conduct stratified sampling. When notNULL
, each resample is created within the stratification variable. Numericstrata
are binned into quartiles.- breaks
A single number giving the number of bins desired to stratify a numeric stratification variable.
- pool
A proportion of data used to determine if a particular group is too small and should be pooled into another group. We do not recommend decreasing this argument below its default of 0.1 because of the dangers of stratifying groups that are too small.
- ...
These dots are for future extensions and must be empty.
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
With more than one repeat, 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: three groups of 10 that are generated
separately.
With a strata
argument, the random sampling is conducted
within the stratification variable. This can help ensure that the
resamples have equivalent proportions as the original data set. For
a categorical variable, sampling is conducted separately within each class.
For a numeric stratification variable, strata
is binned into quartiles,
which are then used to stratify. Strata below 10% of the total are
pooled together; see make_strata()
for more details.
Examples
vfold_cv(mtcars, v = 10)
#> # 10-fold cross-validation
#> # A tibble: 10 × 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 × 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