randomforest VS m2cgen

I did end up writing and using a custom library for Random Forest (it's also in AwesomGo) in one real-world project (detecting Alzheimer's and Parkinson's from speech from a mobile app) - https://github.com/malaschitz/randomForest I had better results than the team who used TensorFlow and most importantly I didn't have to use any other technology than Go. For NN's it's probably best to use https://gorgonia.org/ - but it's not exactly a user friendly library. But there is a whole book on it - Hands-On Deep Learning with Go.

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