EvoTrees.jl: Efficient Boosted Trees on CPUs & GPUs in Julia | Desgagne-Bouchard
Автор: The Julia Programming Language
Загружено: 2025-12-17
Просмотров: 69
EvoTrees.jl: Efficient Boosted Trees on CPUs & GPUs in Julia by Jeremie Desgagne-Bouchard
PreTalx: https://pretalx.com/juliacon-2025/tal...
Key topics covered:
1. Key steps in gradient-boosted trees algorithm: binarization, build histogram, best split search, prediction.
2. EvoTrees approach to reconcile ease of research and achieving performance comparable to its C++ peers
3. Minimal benchmark against `XGBoost`, `LightGBM` and `CatBoost`
4. Beyond gradient-based learning: mean-absolute error and volatility-adjusted losses
5. Future development paths: improved GPU acceleration, auto-diff support for custom and multi-target losses and enhanced support of categorical variables.
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