container for information specifying a cross-validated machine learning exercise
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a string, "LOO" indicating leave-one-out cross-validation, or "LOG" indicating leave-out-group, or "NOTEST", indicating the entire dataset is used in a single training run.
numeric specification of the number of
cross-validation iterations to use. Ignored if
function, with parameters data (bound
to data.frame), clab (bound to character string), iternum (bound
to numeric index into sequence of 1:
function, with parameters formula, data. The function must return a formula suitable for defining a model on the basis of the main input data. A candidate fsFun is given in example for fsHistory function.
type == "LOO", no other parameters are inspected.
type == "LOG" a value for
partitionFunc must be
supplied. We recommend using
K in this usage must be the same.
This redundancy will be removed in a future upgrade.
parallel package is attached and symbol
mc_fork is loaded, cross-validation will
be distributed to cores using
An instance of
classifierOutput, with a special
RObject return slot is populated with a
niter cross-validation results. Each element of this list
is itself a list with two elements:
test.idx (the indices
of the test set for the associated cross-validation iteration,
classifierOutput generated at
each iteration. Thus there are
instances nested within the main
xvalSpec is used.
Vince Carey <email@example.com>
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