Answer: Need help with MLearn in MLInterfaces package
10.5 years ago by
That's a great question! I don't think MLInterfaces has an easy
for that at a high level, but one could be added for the next release
is coming soon. Perhaps MLPredict().
In the mean time, you can always get the "native" output of a
RObject. (If you are using a genuine xvalSpec you will need to apply
at two levels, that should be documented in the vignette.) If that
from a family of fitting tools that have a predict(... newdata=...)
you should be able to go forward without too much effort.
A more cunning approach with the software in its current state, for
learning only, is to "forge" class labels for the unclassified samples
fake them in the pData of the ExpressionSet. Then supply a training
index vector at the xvalSpec parameter that includes only the labeled
samples-- the unlabeled samples will not be used to build the
the testPrediction components of the output object will have bona-fide
on these. Of course be sure to ignore the forged labels in any
interpretation -- remove them at once!
On Fri, Mar 13, 2009 at 12:11 PM, Tul Gan <email@example.com> wrote:
> I am trying to use MLearn in MLInterfaces package to do
> randomforest, clustering, knn etc. How do I predict on a test set
> I do not know the classes? My training set has two classes.
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