Your message of "Sun, 08 Sep 2002 01:02:32 EDT."
             <LNBBLJKPBEHFEDALKOLCEEOGBCAB.tim.one@comcast.net>

>> Before we get too far down this road, what do people think of
>> creating a spambayes package containing classifier and tokenizer?
>> This is just to minimize clutter in site-packages.
Guido> Too early IMO (if you mean to leave the various other tools out
Guido> of it).
Well, I mentioned classifier and tokenize only because I thought they were
the only importable modules.  The rest represent script-level code, right?
Guido> If and when we package this, perhaps we should use Barry's trick
Guido> from the email package for making the package itself the toplevel
Guido> dir of the distribution (rather than requiring an extra directory
Guido> level just so the package can be a subdir of the distro).
That would be perfect.  I tried in the naive way last night, but wound up
with all .py files in the package, which wasn't my intent.
Skip

Neil trained a classifier using 3 sets with about 500 ham and spam in each.
We're missing half his test run results due to a cmp.py bug (since fixed);
the "before custom fiddling" figures on the 3 reported runs were:
The "total unique" figures counts all 6 runs; it's just the individual-run
fp and fn percentages we're missing for 3 runs.
Jeremy reported these "before custom fiddling" figures on 4 sets with about
So things are clearly working much better for Neil.  Both reported
significant improvements in both f-n and f-p rates by folding in more header
lines.  Neal added Received analysis to the base tokenizer's header
analysis, and Jeremy skipped the base tokenizer's header analysis completely
but added base-subject-line-like but case-folded tokenization for almost all
header lines (excepting only Received, Data, X-From_, and, I *suspect*, all
When I try 5 random pairs of 500-ham + 500-spam subsets in my test data, I
This is much closer to what Neil saw, but still looks better.  Another run
on a disjoint 5 random pairs looked much the same; total unique fp rose to
12 and fn fell to 27; on a third run with another set of disjoint 5 random
pairs, likewise, with fp 12 and fn 40.  So I'm pretty confident that it's
not going to matter which random subsets of 500 I take from my data.
It's hard to conclude anything given Jeremy's much worse results.  If they
were in line with Neil's results, I'd suspect that I've over-tuned the
algorithm to statistical quirks in my corpora.