applied 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?

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