I'd imagine tanimoto or cosine similarity would get you most of the way there while being very off-the-shelf. If you're going to go the route of binary classif

I'd imagine tanimoto or cosine similarity would get you most of the way there while being very off-the-shelf. If you're going to go the route of binary classification, I'd personally do it via RFs as variable importance (product importance) is built in. (But that's just personal pref) I think that it's a solid step-by-step thought process on tackling the problem -- I'd probably think of the false positive vs false negative in terms of the relative expected values of success/failure in those classifications. (And perhaps cost-sensitivity could even be added to your original classifier -- perhaps if you had a forest of 500 trees, and you get even 100 votes for pregnant, that's enough to decide to send a pregnancy-targeted mailer)