For a vector of training labels, we return a list of crossvalidation folds,
where each fold has the indices of the observations to leave out in the fold.
In terms of classification error rate estimation, one can think of a fold as a
the observations to hold out as a test sample set. Either the hold_out
size or the number of folds, num_folds
, can be specified. The number
of folds defaults to 10, but if the hold_out
size is specified, then
num_folds
is ignored.
1  cv_partition(y, num_folds = 10, hold_out = NULL, seed = NULL)

y 
a vector of class labels 
num_folds 
the number of crossvalidation folds. Ignored if

hold_out 
the holdout size for crossvalidation. See Details. 
seed 
optional random number seed for splitting the data for crossvalidation 
We partition the vector y
based on its length, which we treat as the
sample size, 'n'. If an object other than a vector is used in y
, its
length can yield unexpected results. For example, the output of
length(diag(3))
is 9.
list the indices of the training and test observations for each fold.
1 2 3 4 5  # The following three calls to \code{cv_partition} yield the same partitions.
set.seed(42)
cv_partition(iris$Species)
cv_partition(iris$Species, num_folds = 10, seed = 42)
cv_partition(iris$Species, hold_out = 15, seed = 42)

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