imodels VS Gorgonia

- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications

Next, this variable will be your target and you can use various supervised learning models to answer your question. Since interpretation is key, you can use something from here: https://github.com/csinva/imodels or do some black box models and use shab to understand which features contributed most.

Option 2) fit a model from https://github.com/csinva/imodels on the predicted values of the RF

Despite recent breakthroughs in the formulation and fitting of interpretable models, implementations are frequently challenging to locate, utilize, and compare. imodels solves this void by offering a single interface and implementation for a wide range of state-of-the-art interpretable modeling techniques, especially rule-based methods. imodels is basically a Python tool for predictive modeling that is simple, transparent, and accurate. It gives users a straightforward way to fit and use state-of-the-art interpretable models, all of which are compatible with scikit-learn (Pedregosa et al., 2011). These models can frequently replace black-box models while boosting interpretability and computing efficiency without compromising forecast accuracy. Continue Reading

Our package imodels is expanding our sklearn-compatible set of interpretable models and always looking for new contributors!

GitHub - gorgonia/gorgonia: Gorgonia is a library that helps facilitate machine learning in Go.

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.

Perhaps it's a product of French culture, but because Gorgonia[0] has a number of French contributors, this was actually the way we structured our documentation.

But this is the first time I've heard of the name of the framework.

[0]: https://gorgonia.org

Oh it's.Gorgonia

Website: https://gorgonia.org

I use Gorgonia.

What you think about this https://github.com/gorgonia/gorgonia ? I also recall there is something else out there but can't find it at the moment...

Yep, all of them: https://github.com/gorgonia/gorgonia