SpamBayes is a tool used to segregate unwanted mail (spam) from the mail you want (ham). Before SpamBayes can be your spam filter of choice you need to train it on representative samples of email you receive. After it's been trained, you use SpamBayes to classify new mail according to its spamminess and hamminess qualities. It's best to train on recent email, because your interests and the nature of what spam looks like change over time.

To configure the Outlook plugin, you should choose SpamBayes Manager from the SpamBayes button on the SpamBayes toolbar. If you use the POP3 proxy or IMAP filter, then simply open a browser window to http://localhost:8880, click on the configuration link on the top right of the page that opens up, and fill in the details. With the POP3 proxy, when you need to select local port numbers to proxy on, if you are only proxying one server, then try 110 first.

Unless you want to run from the source code, all you need is the SpamBayes installer. At present, any Windows users using Outlook or using any other mail client and retrieving mail via POP3 can use the installer. At present, all other users must run from source. If you want to run from source, you must have a recent version of Python installed on your computer, version 2.2 or later. (Don't ask about backporting it to earlier versions of Python. It's almost a certainty this won't happen.

SpamBayes will work with most POP3 or IMAP compatible clients. How you implement depends on your local architecture. Users with access to procmail can just write a recipe that invokes SpamBayes like this: :0fw | /opt/spambayes/sb_filter.py Follow that with a recipe to check the results and take action: :0 * ^X-Spambayes-Classification: spam ${MAILDIR}/spam Emacs and XEmacs both come with VM, one of a choice of several Emacs-based mail packages. Emacs is extensible using Emacs Lisp or Pymacs.

You can't use SpamBayes directly with the normal AOL Mail interface, but AOL's Mail system is reputed to allow IMAP access. If that proves to be correct you should be able to use the SpamBayes IMAP filter (sb_imapfilter.py) to scrub your AOL mailbox. Should you try this, reports of your successes or failures on the spambayes@python.org mailing list would be appreciated.

Note: The following instructions have been verified using Eudora 5.1 under Windows. If anyone is using Eudora under Mac OS X please let us know if the configuration is the same as Windows. Eudora does not allow configuring the server port through the normal options dialogue. However a large number of options are exposed in an initialization file (eudora.ini) read at startup. The contents of the initialization file are documented by clicking on Help->Topics and searching on EUDORA.

If you subscribe to Yahoo! Mail Plus, you have the option of accessing your Yahoo! mail via the POP3 protocol. SpamBayes has a POP3 proxy application called sb_server that will allow you to filter any POP3 mail account using a POP3 client of your choice (Outlook, Outlook Express, Netscape, Mozilla, Eudora, etc.). If you use the free Yahoo! Mail service, then Yahoo no longer provides POP3 access.

Follow the "Review messages" link and you'll see a list of the emails that the system has seen so far. Check the appropriate boxes and hit Train. The messages disappear and if you go back to the home page you'll see that the "Total emails trained" has increased. Once you've done this on a few spams and a few hams, you'll find that the X-Spambayes-Classification header is getting it right most of the time.

Instructions about training the Outlook plugin can be found in the documentation for the plugin, and the 'Configuration Wizard' will attempt to guide you through an initial training process. Basically what you need to do is move as much spam as you have into your spam folder, tell the plugin which folder that is and which folders contain examples of ham, and it will do the rest. The plugin does not train on all incoming mail.

SpamBayes looks for your configuration file in three places - if it can't find it, then, obviously, your options will not be loaded. The first place that SpamBayes checks is the environment variable BAYESCUSTOMIZE. You can set this to the path of your configuration file, wherever it is, and it will be loaded. You can also specify more than one file, separated by the appropriate path separator for your platform.

Make a note of what you currently have in the "incoming mail server" entry (the name of your POP3 server). Change the "incoming mail server" from the name of your POP3 server to "localhost" (no quotes). Open the SpamBayes configuration page at http://localhost:8880/config (you can do this via the tray application, or just type that address into your browser). Enter your POP3 server's name in the top box ("Remote Servers").

To subscribe, visit the relevant page referenced in the previous Q&A and follow the directions there. To unsubscribe, visit the same link. Scroll to the bottom of the page and enter your email address in the last field then click the "Unsubscribe or edit options" button. From there you can simply click the "Unsubscribe" button. A confirmation mail will be sent to your email address. To confirm the unsubscribe request, simply reply to that message.

A database. Loosely speaking, this is a collection of words and associated spam and ham probabilities. The database says "If a message contains the word 'Viagra' then there's a 98% chance that it's spam, and a 2% chance that it's ham." This database is created by training - you give it messages, tell it whether those messages are ham or spam, and it adjusts its probabilities accordingly. How to train it is covered below. By default it lives in a file called "hammie.

The SpamBayes Outlook plug-in simply watches the folders that you have instructed it to for new mail. When new mail is received, Outlook informs SpamBayes, which then scores the message and performs the actions you have asked it to, depending on the message score. Thus it isn't involved in the delivery of mail, and so has no idea that it is coming from Exchange.

Sorry, but you can't. However, Outlook has an excellent "auto-archive" facility which can be used to the same effect - simply configure auto-archive to periodically delete your Spam folder. It is recommended that you configure auto-archive to keep at least a few days of Spam around, should the SpamBayes database become corrupt and require you to perform a full re-train.

Instructions for doing this can be found in the "Configuration Guide". (You get to this by doing SpamBayes->Help->About SpamBayes, which opens up a browser window, then clicking the "Configuration Guide" link. The appropriate section is headed "Multiple Configuration Files" and is at the end of the document). Basically, you need to create a file "default_configuration.

Alternatively, when you receive an incorrectly classified message, you can forward it to the SMTP proxy for training. If the message should have been classified as spam, forward or bounce the message to spambayes_spam@localhost, and if the message should have been classified as ham, forward it to spambayes_ham@localhost. You can still review the training through the web interface, if you wish to do so.

Given a pair of Unix mailbox format files (each message starts with a line which begins with 'From '), one containing nothing but spam and the other containing nothing but ham, you can train SpamBayes using a command like: python sb_mboxtrain.py -g ~/tmp/newham -s ~/tmp/newspam The above command is OS-centric (e.g., UNIX, or Windows command prompt). You can also use the web interface for training as detailed above.

Initially, SpamBayes will not be able to distinguish spams from hams. With no training inputs, the classifier will simply mark everything unsure. Once you start training the classifier on a representative set of spams and hams it should very quickly begin to improve, however. If that's not the case, perhaps you have something misconfigured. Here are a couple things to check: What do you have your ham and spam thresholds set to? The defaults are 0.2 and 0.

In theory, yes it can, though this has not yet been tried. There are a couple other tools, POPFile and CRM114. A demonstration script which performs n-way classification in also in the contrib directory of the SpamBayes source.

The problem with a server-side solution is that everyone has a different idea of what is spam - that's the whole strength of the bayesian-style filtering concept. If you are certain that all of your users would agree on what is spam and what is not, then this might work for you, but otherwise you really have to have individual databases for each user. Either way, you should be able to modify SpamBayes easily enough to fit into your setup.

The main reason is that for the most part SpamBayes doesn't need it! As long as you keep training on unsure or mis-classified mail, SpamBayes will learn what you consider good mail without needing any specific lists. In addition, tokens are generated from email addresses, so an automatic 'whitelist' (of sorts) is generated, as is a similar blacklist. Whitelists and blacklists are problematic anyway, because 'spoofing' (pretending you are someone else) is reasonably simple, and also very common.

It may be obvious to you that the message is spam, but the classifier only works on the information it has been given. Maybe this is "new" (you've never seen this particular flavor of spam before), or maybe there aren't enough clues in the message which the system is aware of as strong spam clues. You should look at the clues that SpamBayes generated, and that should give you an idea of the reason for the classification.

A reasonably small amount of messages posted to spambayes@python.org are spam, bounce messages, out-of-office messages, and so on. People often wonder (and ask) why we don't filter these out, or require all posters to be subscribers to the mailing list, or moderate the list. No filtering is done because spam is often discussed on the list. For example, you can send in a spam message that you received, with the clues that it generated, and ask why it scored what it did.