A function to execute multiple "1 vs. all" binary tasks.
doMulti(object, top, method, ...)# S4 method for ExprsMulti
doMulti(object, top, method, ...)
Specifies the ExprsArray object to undergo feature selection.
A numeric scalar or character vector. A numeric scalar indicates
the number of top features that should undergo feature selection. A character vector
indicates specifically which features by name should undergo feature selection.
Set top = 0 to include all features. A numeric vector can also be used
to indicate specific features by location, similar to a character vector.
A character string. The ExprsBinary method to execute multiple times.
Arguments passed to the respective wrapped function.
A list of the results given by method.
ExprsMulti: Method to execute multiple "1 vs. all" binary tasks.
doMulti depends on the total number of levels in the
$defineCase factor. If a training set is missing any
one of the factor levels (e.g., owing to random cuts during
cross-validation), the ExprsModule component that
would refer to that class label gets replaced with an NA
placeholder. This NA placeholder gets handled as a
special case when predicting with an ExprsModule.
During ExprsModule class prediction, the absence
of a class during training (i.e., an NA placeholder)
will prevent an ExprsModule object from possibly
predicting that class in a validation set. Rather, an
ExprsModule can only make predictions about class
labels that it "knows". However, all "unknown" classes
in the validation set (i.e., those missing from the training
set) still impact metrics of classifier performance.
An ExprsModule object can only make predictions on
an ExprsMulti object with the same number of recorded
class labels (i.e., the total number of levels in the
$defineCase factor). As with all functions included
in this package, all ties get broken using probability
weights proportional to the relative class frequencies
in the training set.
fs
build
doMulti
exprso-predict
plCV
plGrid
plGridMulti
plMonteCarlo
plNested