Making binary prediction with GPBoost (or MERF) My question is regarding this post from 1.5 years ago: Modelling clustered data using boosted regression trees
My label is a binary variable (yes/no). Is it possible to use GPBoost / MERF in order to make predictions for this binary variable? I wasn't able to find an answer to this question really quick. If no, which multilevel/mixed effects models can do this? I've already found Mixed Effects Logistic Regression.
Thanks,
Kind regards,
Olivier

Yes, we can use binary response variables. In GPBoost we set likelihood="binary". MERF in theory can also handle it, as based on the MERF's author's PhD thesis here binary outcomes are within the scope of the methodology but I see no such option in the MERF package itself.
I would suggest looking at bambi (in Python/PyMC3) or rstanarm (in R/Stan) too, you can define splines for your main fixed effects so they have non-linear influence in the response but also have a full MCMC approach in fitting the model and check the relevant diagnostics. (statsmodels often provides sub-optimal fitting results when dealing with mixed models.)
