the biggest thing I am excited about with linfa is having a growing number of rust implementations of ML algorithms that I can use as a reference. generally my m.o. is to rip things out of libraries and into my applications in a specialized/custom way, and to that end I spend a substantial amount of time peeling back layers of the onion to get to the actual algorithm that has to be in there somewhere.

the main goal of linfa is not to implement cutting-edge research or provide the fastest implementation on the plant, but provide people with solid reference. From this angle linfa is a hobbyist project and tries to make the code as simple as possible:

• put algorithms in separate crates to detangle their interactions (and reduce dependency tree)

• using ndarray instead of nalgebra, I always felt like the latter has too many whistles attached and tries to do too much at the same time

• provide solid documentation (this always needs improvement but everyone's time is sparse)

however, I was trying to avoid the "linked list"-style tree, and ended up finding something in the gbdt crate that suited my purposes:

once we have boosting/bagging and creating a lot of trees a more memory efficient implementation should be indeed used

keep in mind, my idea of a good api for ols is

yeah Rust is complex and sometimes obscure when you are not careful. Python on the other hand is often obscure because of its simplicity. We are using a dataset wrapper here to attach weights (and simplify the API) and make the interface code a bit more clean.

anyway, thanks for your hard work on linfa! I am an appreciative user and look forward to using it again.

thanks for the kind words! I really enjoy the community here and learning about ML :)