3.8   Do an Initial Training

Before you can use SpamSieve, you should give it some examples of messages you consider to be spam, and ones which you do not. You do this by selecting some messages and choosing a training command from the menu. SpamSieve collects information from the messages it’s trained with into its corpus, which it uses to predict whether subsequent messages are spam. Don’t worry; it learns quickly!

Go to SpamSieve’s Filter menu and choose Show Statistics. The Corpus section at the bottom of the Statistics window shows how many good and spam messages SpamSieve has been trained with, and what percentage of them are spam. After the initial training, SpamSieve will automatically train itself, and you’ll only need to train it to correct mistakes.

To do the initial training, use both spam and good messages subject to two requirements:

Do not use more than 1,000 messages.

Using up to 1,000 recent messages in the initial training lets SpamSieve start out with a high level of accuracy. In general, the more messages you train SpamSieve with, the better its accuracy will be. However, using more than 1,000 messages initially, would “fill up” SpamSieve’s corpus with older messages, making it slower and less effective at adapting to new kinds of spam that you’ll receive in the future.

The messages should be approximately 65% spam.

For example, use 650 spams and 350 good messages, 195 spams and 105 good messages, or 65 spams and 35 good messages. It is better to use fewer messages in the initial training (i.e. not use all your saved mail) than to deviate from the recommended percentage. For example, if you have 500 good messages but only 195 saved spam messages, don’t train SpamSieve with all 695 messages. Instead, train it with the 195 spams and about 105 representative good messages.

After the initial training, you don’t have to worry about the number or percentage of messages in the corpus. SpamSieve will automatically learn from new messages as they arrive and keep its corpus properly balanced.

Accuracy will improve with time, but if you’ve used at least 100 or so messages in the initial training, SpamSieve should immediately start moving some of the incoming spam messages to your spam folder. If you don’t see results right away, check the setup in your mail program. After a few hundred messages of each type are in the corpus, SpamSieve should be catching most of your spam.

Now you’re done setting up SpamSieve. The Correct All Mistakes section explains how you can keep SpamSieve’s accuracy high by telling it if it puts any messages in the wrong mailbox.