On Oct 11, 2011, at 1:47 AM, Robin Anil wrote: > Could be due to the way normalization is done.
In what part of the process? > How is CNB performing? It's better, like 40% correct, 60% wrong, but still not good. > Do > share the confusion matrices and per label precision. Usually on the order of 0.05 correct, 95% wrong. If I bring the --maxItemsPerLabel (PrepEmailVectorsDriver) down to about 1000, then I get better results, but still not better than guessing. The main issue is that many of the mail archives have a ton of entries, but then a few only have less than 1000. On the flip side, 1000 is not really enough training wise. If I restrict down the input to mailing lists that have at least 10K items, then I get much better results. Of course, this is expected. The main issue is I don't understand why it would be picking the labels with the least amount of data. > > On Mon, Oct 10, 2011 at 11:20 PM, Grant Ingersoll <gsing...@apache.org>wrote: > >> I was trying the Naive Bayes classifier via the build-asf-email.sh file I >> committed the other day on a data set that had a fairly significant >> variation in the number of messages per training label and am noticing >> (still need to validate more) that the label with the least number of >> examples is often dominating the results. This seems counterintuitive to >> me. I would have expected the largest set would have dominated the results. >> If I even out the number of items per label, than I get reasonable results. >> Any thoughts on what I am seeing? If you are interested, I can share the >> details of the runs. >> >> -Grant >> -------------------------------------------- Grant Ingersoll http://www.lucidimagination.com Lucene Eurocon 2011: http://www.lucene-eurocon.com
