# Extremely Fast Decision Tree

arXiv: https://arxiv.org/abs/1802.08780 · HTML: https://mlsanity.com/paper/1802.08780/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2018.

## Built on by (most cited first, 30 of 43)

- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021)
- [PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams](https://mlsanity.com/paper/2109.05013/) (2021)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent](https://mlsanity.com/paper/2102.07567/) (2021)
- [DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices](https://mlsanity.com/paper/2502.14011/) (2025)
- [A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems](https://mlsanity.com/paper/2210.01985/) (2022)
- [In-context Learning of Evolving Data Streams with Tabular Foundational Models](https://mlsanity.com/paper/2502.16840/) (2025)
- [On-device edge learning for IoT data streams: a survey](https://mlsanity.com/paper/2502.17788/) (2025)
- [Bridging Streaming Continual Learning via In-Context Large Tabular Models](https://mlsanity.com/paper/2512.11668/) (2025)
- [Dynamic Model Tree for Interpretable Data Stream Learning](https://mlsanity.com/paper/2203.16181/) (2022)
- [Learning from Data Streams: An Overview and Update](https://mlsanity.com/paper/2212.14720/) (2022)
- [Online hierarchical partitioning of the output space in extreme multi-label data stream](https://mlsanity.com/paper/2507.20894/) (2025)
- [MABSplit: Faster Forest Training Using Multi-Armed Bandits](https://mlsanity.com/paper/2212.07473/) (2022)
- [Des-q: a quantum algorithm to provably speedup retraining of decision trees](https://mlsanity.com/paper/2309.09976/) (2023)
- [Green Accelerated Hoeffding Tree](https://mlsanity.com/paper/2205.03184/) (2022)
- [Online Learning of Decision Trees with Thompson Sampling](https://mlsanity.com/paper/2404.06403/) (2024)
- [Hoeffding adaptive trees for multi-label classification on data streams](https://mlsanity.com/paper/2410.20242/) (2024)
- [Fully-Dynamic Decision Trees](https://mlsanity.com/paper/2212.00778/) (2022)
- [Efficient Online Decision Tree Learning with Active Feature Acquisition](https://mlsanity.com/paper/2305.02093/) (2023)
- [A Natural Gas Consumption Forecasting System for Continual Learning Scenarios based on Hoeffding Trees with Change Point Detection Mechanism](https://mlsanity.com/paper/2309.03720/) (2023)
- [Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification](https://mlsanity.com/paper/2404.15836/) (2024)
- [Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks](https://mlsanity.com/paper/2502.20627/) (2025)
- [Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles](https://mlsanity.com/paper/2605.12803/) (2026)
- [Bounded Context Management for Tabular Foundation Models on Stream Learning](https://mlsanity.com/paper/2606.18677/) (2026)
- [Shrub Ensembles for Online Classification](https://mlsanity.com/paper/2112.03723/) (2021)
- [Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees](https://mlsanity.com/paper/2302.03994/) (2023)
- [QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest](https://mlsanity.com/paper/2406.12008/) (2024)
- [Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams](https://mlsanity.com/paper/2411.04812/) (2024)
- [A Frank System for Co-Evolutionary Hybrid Decision-Making](https://mlsanity.com/paper/2503.06229/) (2025)

## Abstract (arXiv)

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree---"Extremely Fast Decision Tree", a minor modification to the MOA implementation of Hoeffding Tree---obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.