the new XGBoost wrapper

Gradient boosted trees are a wonderfully flexible tool for machine learning and XGBoost is a state-of-the-art, widely used C++ implementation. Thanks to the library's C bindings, XGBoost has been usable from Julia for quite a long time. Recently, the wrapper has been rewritten as 2.0 and offers many fun new features, some of which were previously only available in the Python, R or JVM wrappers.

We will discuss some new features as of 2.0 of the package including:

• More flexible training via public-facing calls for single update rounds.
• Tables.jl compatibility.
• Automated Clang.jl wrapping of the full libxgboost.
• Introspection of XGBoost internal data (DMatrix, now an AbstractMatrix).
• Handling of missing data.
• Introspection of the trees themselves via AbstractTrees.jl compatible tree objects.
• Updated feature importance API.
• Now fully documented!
• Upcoming GPU support.