I don't know this code too much, but, there is simply a step in front I believe that vectorizes text with TF-IDF. The result are simple vectors. You could just inject your vectors (i.e. real-value attributes) at that stage and skip the TF-IDF. It may need a little hacking.
On Tue, Jul 31, 2012 at 6:21 PM, Eric Friedman <[email protected]> wrote: > All of the examples that I've found for training NB classifiers seem > to have textual data as input. Is there a way to build a classifier > with more general attributes? > > I found this jira ticket > (https://issues.apache.org/jira/browse/MAHOUT-286), but it's been > closed:duplicate under > https://issues.apache.org/jira/browse/MAHOUT-155, which doesn't seem > to address the underlying question. > > I know that I can do this with weka, but not at scale -- is mahout > only able to build textual classifiers? > > Thanks, > Eric
