I feel like a dope for asking this, but after reading various articles I'm 
still not quite sure I get it. What is the relationship or similarity 
between a "probability" prediction from a classfication random forest and a 
Bayesian posterior probability? Can they be considered similar, or would I 
need to take that probability and project it through a likelihood (I'm 
thinking, perhaps wrongly, of computing this from the confusion matrix) to 
get a posterior probability?

Something about that feels wrong, and not being a real statistician (Just a 
shrink with some applied stat training) I can't quite connect the dots. 

Before, this, I've been playing with RF as a way to get a feel for what's 
going on in the data, rather than for making any real-world predictions. I 
just don't want to over-interpret (or misinterpret) those probabilities. 
>From Breiman's site, this is probably clear to a real statistician, but 
someone like me needs to be clobbered over the head with it :-/.

Wisdom Welcome!

Humbly, 

DVB

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