[jmarshall]

A naive Bayes classifier typically makes the assumption that your predictors are independent. This is a huge assumption, and greatly simplifies finding the probability distribution you're interested in for Bayes Thm, namely P(predictors|class). However, they can perform incredibly badly if the assumption is wrong, but only if the dependent predictors end up telling you conflicting things IIRC.

Classification trees basically allow a hierarchical structure to the prediction - i.e. they find the predictor that tells you the most about the likely class first, and split the data based on that. Then in each branch it repeats. This allows each branch to have different predictors (interactions between predictors) and they also deal fine with factors vs numerical variables.

I suspect both will give a fairly solid classification rate - there's so much data here that it'll be hard to screw it up too badly I would think

Cheers,
Jonathan