V-Fold Cross-Validation
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, pool = 0.1, ...)
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. |
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. |
... |
Not currently used. |
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 by default.)
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.
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).
vfold_cv(mtcars, v = 10)
vfold_cv(mtcars, v = 10, repeats = 2)
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")
})
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")
})
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")
})Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.