go-featureprocessing VS m2cgen

Tabular data is well supported in Go. Tabular data and small sample sizes is a sweet spot for Go. You may squeze in some meaningful performance and good looking code and integrations. Some entry points: (1) https://github.com/nikolaydubina/go-ml-benchmarks (2) https://github.com/dmitryikh/leaves (3) https://github.com/nikolaydubina/go-featureprocessing

What is this?

Fast feature preprocessing in Go with feature parity to sklearn

https://github.com/nikolaydubina/go-featureprocessing

What is new?

* Added batch processing

Check out: https://github.com/BayesWitnesses/m2cgen

Notice that I’m using random values to populate the Sentiment field. You might compute the values for your fields based on other fields or actually use an ML model to perform the transformation. E.g. you could make use of m2cgen to transform trained models to pure python code and load them in **RedisGears **to be executed in a *GearsBuilder *instance. Another option is to pull out the big guns and go straight to RedisAI.

I wish that it was more common for model outputs to be converted the way bayeswitness does with mc2gen https://github.com/BayesWitnesses/m2cgen

And that’s it! All the magic in just two lines of code. I would like to thank the authors of the m2cgen library and encourage you to try it out.

I forgot to update the README. We just replaced RNN with xgboost that has a better f1 and is very quick, as the decision trees are translated to plain rust using m2cgen.

Probably, as many frameworks come with a Rust support (or there are wrappers). Some models, like decision tree, can also be automatically translated to plain Rust (in my company we use m2cgen to translate xgboost models to plain rust code).

These repositories on GitHub are good start I think: https://github.com/BayesWitnesses/m2cgen and https://github.com/vickylance/dart_nn