trees VS morphy

I've always wanted my own gradient boosting machine implementation to compete with Xgboost and LightGBM. There's a huge space of unexplored tricks around regularization, randomization, tree structure, etc., that few people are exploring because neural nets are exploding.

So far I've roughly caught up in speed and accuracy with a few original tricks (and 1/20 of the features), but no real breakthroughs: https://github.com/benpastel/trees

On the plus side

This is so true but my biggest struggle. Everything I have just shipped before I thought it was ready did really well, yet I still struggle to do it. I built my own static site generator, which I use for my own blog: https://github.com/kiramclean/morphy

I need to at least add documentation, and obviously there's a million other things I think I need before I tell anyone else about it.

I recently set up code coverage reporting with Codecov for a Clojure project of mine that uses tools.deps and builds on CircleCI. It turned out to be pretty easy but the documentation for the various parts was a bit ambiguous, so I wrote down the steps here in case you're looking to do the same. You can see all the changes it took together in context in this commit where I set it up for my project.