mlsanityData to 14 Oct 2026

Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library

arXiv 2212.02934 ↗First posted 6 Dec 2022cs.LGcited by 13

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Abstract (arXiv)

Yggdrasil Decision Forests is a library for the training, serving and interpretation of decision forest models, targeted both at research and production work, implemented in C++, and available in C++, command line interface, Python (under the name TensorFlow Decision Forests), JavaScript, Go, and Google Sheets (under the name Simple ML for Sheets). The library has been developed organically since 2018 following a set of four design principles applicable to machine learning libraries and frameworks: simplicity of use, safety of use, modularity and high-level abstraction, and integration with other machine learning libraries. In this paper, we describe those principles in detail and present how they have been used to guide the design of the library. We then showcase the use of our library on a set of classical machine learning problems. Finally, we report a benchmark comparing our library to related solutions.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 9 of its 38 references that are in the map. First posted on arXiv 6 Dec 2022.

Builds on

9 of 38 references.

References · 9

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2014OpenML: networked science in machine learningcited by 526
  3. 2019Neural Oblivious Decision Ensembles for Deep Learning on Tabular Datacited by 203
  4. 2015ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and Rcited by 172
  5. 2015Sparse Projection Oblique Randomer Forestscited by 15
  6. 2020Learning Representations for Axis-Aligned Decision Forests through Input Perturbationcited by 4
  7. 2017TF Boosted Trees: A scalable TensorFlow based framework for gradient boostingcited by 1
  8. 2018Exact Distributed Training: Random Forest with Billions of Examplescited by 1
  9. 2020Modeling Text with Decision Forests using Categorical-Set Splitscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 5; 2026: 3.

13 papers.

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  1. 2023A Comparison of Decision Forest Inference Platforms from A Database Perspectivecited by 3
  2. 2023Local-Global Methods for Generalised Solar Irradiance Forecastingcited by 1
  3. 2025A Bring-Your-Own-Model Approach for ML-Driven Storage Placement in Warehouse-Scale Computerscited by 1
  4. 2025Is it Bigger than a Breadbox: Efficient Cardinality Estimation for Real World Workloadscited by 1
  5. 2024Boosting gets full Attention for Relational Learningcited by 0
  6. 2024Relationships are Complicated! An Analysis of Relationships Between Datasets on the Webcited by 0
  7. 2024Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attackscited by 0
  8. 2025ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understandingcited by 0
  9. 2025Classification of Cattle Behavior and Detection of Heat (Estrus) using Sensor Datacited by 0
  10. 2025InferF: Declarative Factorization of AI/ML Inferences over Joinscited by 0
  11. 2026Vectorized Dynamic Histograms for Sparse Oblique Forestscited by 0
  12. 2026Fuzzy Accuracy Compensates for Label Subjectivity in Classification of Skin Tone Using Wearable Photoplethysmography Signalscited by 0
1 more
  1. 2026TreeWalker: Partial Evaluation for Grouped Tree-Ensemble Inferencecited by 0