Randomly partitions data for cross-validation.
For a vector of training labels, we return a list of cross-validation 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
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.
a vector of class labels
the number of cross-validation folds. Ignored if
the hold-out size for cross-validation. See Details.
optional random number seed for splitting the data for cross-validation
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
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.
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