# Dynamic Model Tree for Interpretable Data Stream Learning

arXiv: https://arxiv.org/abs/2203.16181 · HTML: https://mlsanity.com/paper/2203.16181/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [Extremely Fast Decision Tree](https://mlsanity.com/paper/1802.08780/) (2018)
- [On Baselines for Local Feature Attributions](https://mlsanity.com/paper/2101.00905/) (2021)
- [Connecting Interpretability and Robustness in Decision Trees through Separation](https://mlsanity.com/paper/2102.07048/) (2021)
- [Learning Parameter Distributions to Detect Concept Drift in Data Streams](https://mlsanity.com/paper/2010.09388/) (2020)
- [Stochastic Gradient Trees](https://mlsanity.com/paper/1901.07777/) (2019)
- [Leveraging Model Inherent Variable Importance for Stable Online Feature Selection](https://mlsanity.com/paper/2006.10398/) (2020)
- [Adaptive XGBoost for Evolving Data Streams](https://mlsanity.com/paper/2005.07353/) (2020)
- [A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees](https://mlsanity.com/paper/1809.09703/) (2018)
- [Emergent and Unspecified Behaviors in Streaming Decision Trees](https://mlsanity.com/paper/2010.08199/) (2020)

## Built on by (most cited first, 6 of 6)

- [Change Detection for Local Explainability in Evolving Data Streams](https://mlsanity.com/paper/2209.02764/) (2022)
- [Learning from Data Streams: An Overview and Update](https://mlsanity.com/paper/2212.14720/) (2022)
- [Standardized Evaluation of Machine Learning Methods for Evolving Data Streams](https://mlsanity.com/paper/2204.13625/) (2022)
- [Fully-Dynamic Decision Trees](https://mlsanity.com/paper/2212.00778/) (2022)
- [Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees](https://mlsanity.com/paper/2302.03994/) (2023)
- [Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection](https://mlsanity.com/paper/2409.10111/) (2024)

## Abstract (arXiv)

Data streams are ubiquitous in modern business and society. In practice, data streams may evolve over time and cannot be stored indefinitely. Effective and transparent machine learning on data streams is thus often challenging. Hoeffding Trees have emerged as a state-of-the art for online predictive modelling. They are easy to train and provide meaningful convergence guarantees under a stationary process. Yet, at the same time, Hoeffding Trees often require heuristic and costly extensions to adjust to distributional change, which may considerably impair their interpretability. In this work, we revisit Model Trees for machine learning in evolving data streams. Model Trees are able to maintain more flexible and locally robust representations of the active data concept, making them a natural fit for data stream applications. Our novel framework, called Dynamic Model Tree, satisfies desirable consistency and minimality properties. In experiments with synthetic and real-world tabular streaming data sets, we show that the proposed framework can drastically reduce the number of splits required by existing incremental decision trees. At the same time, our framework often outperforms state-of-the-art models in terms of predictive quality -- especially when concept drift is involved. Dynamic Model Trees are thus a powerful online learning framework that contributes to more lightweight and interpretable machine learning in data streams.