Sorry for the confusion.

The primary take away was meant to be that a clustered Bayesian classifier (each trained on random samples of training data) offers significant improvements over traditional single Bayesian classifiers. Nothing revolutionary, just wanted to share a practical example that out performs most Bayesian implementations I have came across. I also wanted to share the results of model pruning and that tokenization matters.

I originally coded this without much intent on sharing, so the post write-up could probably use a lot of work. :)