[Python-Dev] The first trustworthy <wink> GBayes results

Tim Peters tim.one@comcast.net
Sat, 31 Aug 2002 02:45:31 -0400


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[Tim, predicting a false-positive rate]
> I expect we can end up below 0.1% here, and with a generous
> meaning for "not spam",

We're there now, and still ignoring the headers.

> but I think *some* of these examples show that the only way to get
> a 0% false-positive rate is to recode spamprob like so:
>
>     def spamprob(self, wordstream, evidence=False):
>         return 0.0

Likewise.

I'll check in what I've got after this.  Changes included:

+ Using the email pkg to decode (only) text parts of msgs, and,
  given multipart/alternative with both text/plain and text/html
  branches, ignoring the HTML part (else a newbie will never get
  a msg thru:  all HTML decorations have monster-high spam
  probabilities).

+ Boosting MAX_DISCRIMINATORS, from 15 to 16.

+ Ignoring very short and very long "words" (this is Eurocentric).

+ Neither counting unique words once nor an unbounded number of times
  in the scoring.  A word is counted at most twice now.  This helps
  otherwise spamish msgs that have *some* highly relevant content, but
  doesn't, e.g., let spam through just because it says "Python" 80
  times at the start.  It helps the false negative rate more, although
  that may really be due to that UNKNOWN_SPAMPROB is too low
  (UNKNOWN_SPAMPROB is irrelevant to any of the false positives
  remaining, so I haven't run any tests varying that yet).

I'll attach a complete listing of all false positives across the 20,000 ham msgs I've
been using.  People using c.l.py as an HTML clinic are out of luck.  I'd personally
call at least 5 of them spam, but I've been very reluctant to throw msgs out of the
"good" archive -- nobody would question the ones I did throw out and replace.

The false negative rate is still relatively high.  In part that comes from getting the
false positive rate so low (this is very much a tradeoff when both get low!), and in
part because the spam corpus has a surprising number of msgs with absolutely nothing in
the bodies.  The latter generate no tokens, so end up with "probability" 0.5.  The only
thing I tried that cut the false negative rate in a major way was the special
parsing+tagging of URLs in the body (see earlier msg), and that was a highly
significant aid (it cut the false negative rate in half).  There's good reason to hope
that adding headers into the scoring would slash the false negative rate.

Full results across all 20 runs; floats are percentages:

Training on Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams
    testing against Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams
    false positive: 0.025
    false negative: 2.10909090909
    testing against Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams
    false positive: 0.05
    false negative: 2.47272727273
    testing against Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams
    false positive: 0.1
    false negative: 2.50909090909
    testing against Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams
    false positive: 0.0500125031258
    false negative: 2.8

Training on Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams
    testing against Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams
    false positive: 0.05
    false negative: 2.8
    testing against Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams
    false positive: 0.075
    false negative: 2.47272727273
    testing against Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams
    false positive: 0.15
    false negative: 2.36363636364
    testing against Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams
    false positive: 0.0500125031258
    false negative: 2.43636363636

Training on Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams
    testing against Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams
    false positive: 0.075
    false negative: 3.16363636364
    testing against Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams
    false positive: 0.075
    false negative: 2.43636363636
    testing against Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams
    false positive: 0.15
    false negative: 2.90909090909
    testing against Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams
    false positive: 0.0750187546887
    false negative: 2.61818181818

Training on Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams
    testing against Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams
    false positive: 0.1
    false negative: 2.65454545455
    testing against Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams
    false positive: 0.1
    false negative: 1.81818181818
    testing against Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams
    false positive: 0.1
    false negative: 2.25454545455
    testing against Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams
    false positive: 0.0750187546887
    false negative: 2.50909090909

Training on Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams
    testing against Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams
    false positive: 0.075
    false negative: 2.94545454545
    testing against Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams
    false positive: 0.05
    false negative: 2.07272727273
    testing against Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams
    false positive: 0.1
    false negative: 2.58181818182
    testing against Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams
    false positive: 0.15
    false negative: 2.83636363636

The false positive rates vary by a factor of 6.  This isn't significant, because the
absolute numbers are so small; 0.025% is a single message, and it never gets higher
than 0.150%.  At these rates, I'd need test coropora about 10x larger to draw any fine
distinction among false positive rates with high confidence.

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