Re: Any way to tell what features contributed the most for a particular decision?

   Thanks! That's what I was afraid of. I was hoping that I could get something
   out of the Bayesean/Markov ones.

   The challenge is, when using this for a client, they'd like to see reports
   of the most significant features. I may try to build a "debug" data
   structure for the hyperspace classifier that holds more information.

Well, if you want to hack, sure.  It's not too bad for Markov/OSB
because those operate with a one-token-at-a-time pipeline.

However, the hyperspace system works on a phase basis- first the whole text is
turned into features, then and only then are the features checked for
significance.  You'd need to carry a lot of baggage across that
divide.... and even then, it would be _wrong_ because in
hyperspace, the individual features are nearly meaningless!  The
meaning is in the confluence of large numbers of features (specificially
in the inverse-luminance calculation.

So, if they ask you for what was significant in hyperspace, show them
the high-scoring example texts, because _those_ truly are the 
significant elements (and NOT the individual words or phrases within those
texts).

   - Bill Yerazunis

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