plCV(array, top, how, fold, ...)ExprsArray object to undergo cross-validation.top = 0 to include all features. A numeric vector can also be used
to indicate specific features by location, similar to a character vector.build method to iterate.fold = 0 to perform leave-one-out cross-validation.how method.plCV performs v-fold or leave-one-out cross-validation for an
ExprsArray object. The argument fold specifies the number
of v-folds to use during cross-validation. Set fold = 0 to perform
leave-one-out cross-validation. This approach to cross-validation
will work for ExprsBinary and ExprsMulti objects alike. The
peformance metric used to measure cross-validation accuracy is the
acc slot returned by calcStats.This type of cross-validation is most appropriate if the ExprsArray
has not undergone any prior feature selection. However, it may also have a role
as an unbiased guide to parameter selection when embedded in
plGrid. If using cross-validation in lieu of an independent test
set in the setting of one or more feature selection methods, consider using
a more "sophisticated" form of cross-validation as implemented in
plMonteCarlo or plNested.
When calculating classifier performance with calcStats, this
function forces aucSkip = TRUE and plotSkip = TRUE.
fs
build
doMulti
exprso-predict
plCV
plGrid
plGridMulti
plMonteCarlo
plNested