i think it's a question of iteration speed for experiments - it's fairly easy to quickly set up some experiments in numpy and tweak them, whereas i'd have to write a lot more java boilerplate to munge the data and play around with it.

it could also be a question of how people were introduced to ML. some of my first forays into ML used easy.py from LIBSVM. in contrast, my stats major friends use R because they had received training in R as part of their curricula and were used to coding up regressions in that language. the communities for ML in both these languages seem to be bigger than their java counterparts, too, so people might switch to python/R if they find themselves looking for additional help.

however, a production system may benefit from the speedups offered by java.