[laichzeit0]

I think one of the main reasons is that there is no good Python library for doing linear mixed effect models. There is no sklearn implementation. There are some libraries that wrap R's lmer (probably using rpy2 or soemthing). The best native Python library I could find is statsmodels, and it has several shortfalls (saving a model to disk consumes hundreds of megabytes, the predict method is useless, it just predicts using the fixed effects, multi-level beyond just 1 group is not even clearly documented, and the syntax sucks if you really do it, nevermind actually implementing a predict method using those random effects). I think once someone does a decent sklearn implementation, it might take off. I've been thinking of doing an implementation for sklearn as a side project, but I'm not an ML researcher, just a practitioner, so it might suck :)

[fho]

I used statsmodels for a while ... it's definitely possible to predict arbitrary inputs, it just a pain to fiddle in the right inputs ...