Show HN: Feature Processing in Go

I find this project quite interesting because sklearn has a good general design including data transformations and it does make sense to provide compatible functionality for Go.

Feature engineering in general is a hot topic and especially if features are not simple hard-coded transformations but rather can be learned from data. For example, I developed a toolkit intended for combining feature engineering and ML:

https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!

(Currently, it is not actively developed and the focus is moved to a similar project - https://github.com/asavinov/prosto - also aimed at data preprocessing and feature engineering)

Maintainer here: I thought it would be interesting to optimise for latency and convenience of feature processing in Go, since many companies have their backend services already in Go and most of business logic happen on single sample granularity. I would be glad if someone find it insightful or even useful. Cheers,

You write that it is "based on sklearn". Does it rely on sklearn functions and requires Python?

How its used