Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library
Paper • 2212.02934 • Published
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UltraGBM: Unified GBDT Library Research
Comprehensive deep research covering ALL algorithmic advances in Gradient Boosted Decision Tree families.
Date: 2026-04-29
Covered Frameworks
- XGBoost (Chen & Guestrin, KDD 2016) — arxiv:1603.02754
- LightGBM (Ke et al., NeurIPS 2017)
- CatBoost (Prokhorenkova et al., 2017/2018) — arxiv:1706.09516
- Yggdrasil/TF-DF (Guillame-Bert et al., KDD 2023) — arxiv:2212.02934
- Recent Advances (2022-2026): GRANDE, NRGBoost, GBRL, NODE, C-GB, DP-EBM, HybridTree
Key Innovations Catalogued
- 22 algorithmic innovations ranked by priority
- Exact equations, pseudocode, and hyperparameters for each
- Master recipe table with implementation complexity vs. impact
- Phased architecture roadmap for UltraGBM implementation
See UltraGBM_Research_Report.md for the full document.
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