As stated, it wasn't intended for an "expert", but a power user. Somebody has to make the decision anyhow of spam versus non-spam in order to make a training set for "learning" based AI. These days you can purchase spam detection systems/services such that training such systems in-house is usually not worth it. They can use rejected messages from thousands of orgs to train their system.
But what I described had additional purposes such as sub-routing to various departments. It was a multi-purpose email categorizer in the early days of spam. Each approach has trade-offs. I'm not sure how you'd apply a "decision tree" using weights in a way that makes sense to a power user. A non-weighted decision tree seems too blunt an instrument. One generally needs multiple "clues" (factors) voting in tandem.
But what I described had additional purposes such as sub-routing to various departments. It was a multi-purpose email categorizer in the early days of spam. Each approach has trade-offs. I'm not sure how you'd apply a "decision tree" using weights in a way that makes sense to a power user. A non-weighted decision tree seems too blunt an instrument. One generally needs multiple "clues" (factors) voting in tandem.