Return-Path: jeremy@alum.mit.edu
Delivery-Date: Sat Sep  7 21:15:03 2002
From: jeremy@alum.mit.edu (Jeremy Hylton)
Date: Sat, 7 Sep 2002 16:15:03 -0400
Subject: [Spambayes] understanding high false negative rate
In-Reply-To: <LNBBLJKPBEHFEDALKOLCOENBBCAB.tim.one@comcast.net>
References: <15738.13529.407748.635725@slothrop.zope.com>
	<LNBBLJKPBEHFEDALKOLCOENBBCAB.tim.one@comcast.net>
Message-ID: <15738.24135.294137.640570@slothrop.zope.com>

Here's clarification of why I did:

First test results using tokenizer.Tokenizer.tokenize_headers()
unmodified. 

Training on 644 hams & 557 spams
      0.000  10.413
      1.398   6.104
      1.398   5.027
Training on 644 hams & 557 spams
      0.000   8.259
      1.242   2.873
      1.242   5.745
Training on 644 hams & 557 spams
      1.398   5.206
      1.398   4.488
      0.000   9.336
Training on 644 hams & 557 spams
      1.553   5.206
      1.553   5.027
      0.000   9.874
total false pos 139 5.39596273292
total false neg 970 43.5368043088

Second test results using mboxtest.MyTokenizer.tokenize_headers().
This uses all headers except Received, Data, and X-From_.

Training on 644 hams & 557 spams
      0.000   7.540
      0.932   4.847
      0.932   3.232
Training on 644 hams & 557 spams
      0.000   7.181
      0.621   2.873
      0.621   4.847
Training on 644 hams & 557 spams
      1.087   4.129
      1.087   3.052
      0.000   6.822
Training on 644 hams & 557 spams
      0.776   3.411
      0.776   3.411
      0.000   6.463
total false pos 97 3.76552795031
total false neg 738 33.1238779174

Jeremy

