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
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
You can pass in any vector(not just a tfidf vector). For example the
asf-email example script using Vectors generated using the randomized
encoding.
--
Robin Anil
On Tue, Jul 31, 2012 at 12:26 PM, Sean Owen sro...@gmail.com wrote:
I don't know this code too much, but, there is simply a
Can you point me to the class I should look at to see how this is done?
On Tue, Jul 31, 2012 at 10:49 AM, Robin Anil robin.a...@gmail.com wrote:
You can pass in any vector(not just a tfidf vector). For example the
asf-email example script using Vectors generated using the randomized
encoding.
its EncodedVectorsFromSequenceFiles.java I believe
--
Robin Anil
On Tue, Jul 31, 2012 at 6:05 PM, Eric Friedman e...@spottedsnake.netwrote:
Can you point me to the class I should look at to see how this is done?
On Tue, Jul 31, 2012 at 10:49 AM, Robin Anil robin.a...@gmail.com wrote:
