[asavinov]

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)

[nikolayasdf123]

Agree, training feature processors is important. I also added basic functions to fit feature transformers automatically based on input, but I would not expect it to be used actively.

Rather, I expect data scientists or analysts will load sample data, cleanse it, visualize in their notebooks, perhaps make sklearn / R pipelines and serialize them and export to JSON. Then go-featureprocessing can use it.

DS people have interactive traning, visualizations in their favorite tools. Backend team has native Go. Win for everyone :)