Exgboost - Gradient Boosted Decision Trees in Elixir

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I’m excited to announce the initial (v0.1.0) release of Exgboost, a gradient-boosted decision tree library powered by XGBoost and Nx.

Source: GitHub - acalejos/exgboost at v0.1.0
Docs: Exgboost — Exgboost v0.1.0

Exgboost is currently based on this commit for the upcoming 2.0.0 release of XGBoost.

Exgboost provides an implementation of XGBoost that works with Nx tensors.

Xtreme Gradient Boosting (XGBoost) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework.

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I’m happy to announce a new release of EXGBoost featuring plotting with EXGBoost.plot_tree/2 and EXGBoost.Plotting.

Thanks to some nice upgrade with ExDoc, there are also new features such as full-text search and Livebook support in the docs.

I’ve included a set of pre-defined styles for the plotting module, which you can see in the docs.

I’ve also release a livebook as a blog post with all of the plots rendered. You can check it out at Plotting XGBoost Trees in Elixir

Since EXGBoost supports loading trained models across different APIs, you can even train using the Python API and then plot using this Elixir API if you prefer.

Let me know what you think!

TLDR:

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Just released v0.5.1, which adds support for backends other than Nx.BinaryBackend

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