That's a great question!  I don't think MLInterfaces has an easy approach
for that at a high level, but one could be added for the next release which
is coming soon.  Perhaps MLPredict().

In the mean time, you can always get the "native" output of a classifier's
result using
RObject.  (If you are using a genuine xvalSpec you will need to apply
RObject
at two levels, that should be documented in the vignette.)  If that object
comes
from a family of fitting tools that have a predict(... newdata=...) idiom,
then
you should be able to go forward without too much effort.

A more cunning approach with the software in its current state, for
supervised
learning only, is to "forge" class labels for the unclassified samples --
just
fake them in the pData of the ExpressionSet.  Then supply a training set
index vector at the xvalSpec parameter that includes only the labeled
samples-- the unlabeled samples will not be used to build the classifier,
and
the testPrediction components of the output object will have bona-fide
predictions
on these.  Of course be sure to ignore the forged labels in any downstream
interpretation -- remove them at once!

On Fri, Mar 13, 2009 at 12:11 PM, Tul Gan <tulgan@ymail.com> wrote:

> Hi,
>           I am trying to use MLearn in MLInterfaces package to do
> randomforest, clustering, knn etc. How do I predict on a test set for which
> I do not know the classes? My training set has two classes.
>
> Thanks,
>
> Tulgan
>
>
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