> > I also looked in more detail at some f-p's in my geeks traffic.  The
> > first one's a doozie (that's the term, right? :-).  It has lots of
> > HTML clues that are apparently ignored.
>
> ?? The clues below are *loaded* with snippets unique to HTML (like '<br>').
I *meant* to say that they were 0.99 clues cancelled out by 0.01
clues.  But that's wrong too!  It looks I haven't grokked this part of
your code yet; this one has way more than 16 clues, and it seems the
classifier basically ended up counting way more 0.99 than 0.01 clues,
and no others made it into the list.  I thought it was looking for
clues with values in between; apparently it found none that weren't
exactly 0.5?
> That sure sets the record for longest list of cancelling extreme clues!
This happened to be the longest one, but there were quite a few
similar ones.  I wonder if there's anything we can learn from looking
at the clues and the HTML.  It was heavily marked-up HTML, with ads in
the sidebar, but the body text was a serious discussion of "OO and
soft coding" with lots of highly technical words as clues (including
Zope and ZEO).
> That there are *any* 0.50 clues in here means the scheme ran out of
> anything interesting to look at.  Adding in more header lines should
> cure that.
Are there any minable-but-unmined header lines in your corpus left?
Or do we have to start with a different corpus before we can make
progress there?
> > The seventh was similar.
> >
> > I scanned a bunch more until I got bored, and most of them were either
> > of the first form (brief text with URL followed by quoted HTML from
> > website)
>
> If those were text/plain, the HTML tags should have been stripped.  I'm
> still confused about this part.
No, sorry.  These were all of the following structure:
multipart/mixed
text/plain        (brief text plus URL(s))
text/html         (long HTML copied from website)
I guess you get this when you click on "mail this page" in some
browsers.
> That HTML tags aren't getting stripped remains the biggest mystery to me.
Still?
> This seems confused: Jeremy didn't use my trained classifier pickle,
> he trained his own classifier from scratch on his own corpora.
> That's an entirely different kind of experiment from the one you're
> trying (indeed, you're the only one so far to report results from
> trying my pickle on their own email, and I never expected *that* to
> work well; it's a much bigger mystery to me why Jeremy got such
> relatively worse results from training his own -- and he's the only
> one so far to report results from *that* experiment).
I think it's still corpus size.
--Guido van Rossum (home page: http://www.python.org/~guido/)