lleaves VS ngboost

I built this decision tree (LightGBM) compiler last summer: https://github.com/siboehm/lleaves

It get's you ~10x speedups for batch predictions, more if your model is big. It's not complicated, it ended up being <1K lines of Python code. I heard a couple of stories like yours, where people had multi-node spark clusters running LightGBM, and it always amused me because by if you compiled the trees instead you could get rid of the whole cluster.

In a near-future version I'll expose some of the compilation parameters, I was somewhat afraid of having an API that's too complicated deterring people who just want a no-fuzz drop-in replacement for LightGBM. But as long as I keep sane defaults and have the parameters optional it should be fine. Relevant parameters are definitely block size (needs to adjust to L1i size and tree size) as well as the LLVM codemodel (a smaller adress space increases single-batch prediction speeds but doesn't work for large models). The thread-size specific compilation I'm still looking into, it makes the API more complicated and so might not be worth it.

For DARKO, I do this by passing my time-decay/padding/kalman calculations as features (along with other pieces of biographical information) into NGboost. This gives me a point-estimate prediction, as well as confidence intervals. NGboost is somewhat limited in terms of what distributions it can handle, so you need to use care here, but it's very powerful since it can easily work with a large feature space (DARKO uses hundreds of features).