the new XGBoost wrapper

07/26/2023, 6:40 PM — 6:50 PM UTC
32-123

Abstract:

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.

Description:

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.

Platinum sponsors

JuliaHub

Gold sponsors

ASML

Silver sponsors

Pumas AIQuEra Computing Inc.Relational AIJeffrey Sarnoff

Bronze sponsors

Jolin.ioBeacon BiosignalsMIT CSAILBoeing

Academic partners

NAWA

Local partners

Postmates

Fiscal Sponsor

NumFOCUS