machine learning

So I have a black box that is throwing streams of network data into two bins. I want to run a bayesian classifier (or some sort of classifier) to try and understand how it makes its choices. The input/output is easy.... I have parsers to break the problem into about 50 binary dimensions. But the problem is I want to tune a classifier and then understand the classification it comes up with in human-ish terms, not as some n-dimensional mapping based on quadratic functions or whatever the fuck you mathematicians do while wanking it.

No, I need actual things like "oie, it always goes bugger when the layer two protocol is six, mate!"

How does one do that?

How are you training it? Do you have multiple test sets? Are you overtraining?
If you're looking for specific characteristics, you're going to need supervise the thing yourself when it's training, or it might never actually become trained to find those characteristics.
In other words use an algorithm to run test sets and count outputs. If it's trained well enough your shit should find all kind of correllations between shits