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<p>Maintaining learning models in dynamic environments requires transparency for trust and compliance, particularly under regulatory frameworks like the Artificial Intelligence (AI) Act by the European Union. Data stream models must balance adaptability with interpretability, and to keep AI models effective in evolving contexts, maintaining transparency is essential. To address this, we introduce Soft Hoeffding Trees (SoHoT) as transparent, differentiable decision trees for data streams. SoHoTs use a novel routing function, leveraging the Hoeffding inequality for tree expansion, while gradient descent updates tree weights to adapt to drifting data distributions. Transparency is further enhanced with decision-rule-based feature importance and a sparse activation function, enabling selective subtree consideration for final predictions. We also provide a visualization of the model’s decision-making process for user interpretability. Evaluated on 20 data streams, SoHoT outperforms Hoeffding trees and competes with Hoeffding adaptive trees and soft trees under AUROC. We also demonstrate how to balance transparency and performance, by looking at the trade-off and measuring prediction performance per complexity, which showcases SoHoT’s benefits compared to existing data stream algorithms.</p>

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Adaptive differentiable trees for transparent learning on data streams

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月19日

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  • Kirsten Köbschall,
  • Lisa Hartung,
  • Stefan Kramer

摘要

Maintaining learning models in dynamic environments requires transparency for trust and compliance, particularly under regulatory frameworks like the Artificial Intelligence (AI) Act by the European Union. Data stream models must balance adaptability with interpretability, and to keep AI models effective in evolving contexts, maintaining transparency is essential. To address this, we introduce Soft Hoeffding Trees (SoHoT) as transparent, differentiable decision trees for data streams. SoHoTs use a novel routing function, leveraging the Hoeffding inequality for tree expansion, while gradient descent updates tree weights to adapt to drifting data distributions. Transparency is further enhanced with decision-rule-based feature importance and a sparse activation function, enabling selective subtree consideration for final predictions. We also provide a visualization of the model’s decision-making process for user interpretability. Evaluated on 20 data streams, SoHoT outperforms Hoeffding trees and competes with Hoeffding adaptive trees and soft trees under AUROC. We also demonstrate how to balance transparency and performance, by looking at the trade-off and measuring prediction performance per complexity, which showcases SoHoT’s benefits compared to existing data stream algorithms.