I've got a decision tree/random forest implementation as well. [1] I originally hacked out go code to analyze forests from other programs but have ended up fini

I've got a decision tree/random forest implementation as well. [1] I originally hacked out go code to analyze forests from other programs but have ended up finishing it off and optimizing it to learn faster then other libraries I've tried for my use cases (wide data with lots of categorical and missing values). The language and tooling (pprof, go fmt, go doc) are great and make it quick to write and optimize stuff so it is well suited for my (largely experimental) purposes. I also really like slices for writing efficient code as they let you pre optimize and reuse arrays and not have to keep track of the ending position. Matrix libraries would be nice but you can call c ones via cgo. I am hopping for efficient pure go ones to be developed eventually so you can use them on app engine/nacl/exacycle or other untrusted code environments. [1] https://github.com/ryanbressler/CloudForest https://github.com/ryanbressler/CloudForest