A function to execute multiple "1 vs. all" binary tasks.
doMulti(object, top = 0, method, ...)
An ExprsArray
object. The training set.
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 method to apply.
Arguments passed to the detailed function.
A list of the results from method
.
doMulti
runs once for each factor level in the
"defineCase" column. 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. Note that this NA placeholder will prevent a
classifier from possibly predicting the NA class (i.e., a
classifier can only make predictions about class
labels that it "knows"). However, these "unknown" classes
still impact metrics of classifier performance.
Otherwise, see exprso-predict
.