Emergent and Unspecified Behaviors in Streaming Decision Trees
This work addresses explainability issues in streaming decision trees for real-time applications, but it is incremental as it focuses on analyzing existing methods rather than introducing new ones.
The authors investigated why Hoeffding trees, state-of-the-art for streaming data, perform well, identifying thirteen unspecified design decisions that significantly impact predictive accuracy without altering the core algorithms.
Hoeffding trees are the state-of-the-art methods in decision tree learning for evolving data streams. These very fast decision trees are used in many real applications where data is created in real-time due to their efficiency. In this work, we extricate explanations for why these streaming decision tree algorithms for stationary and nonstationary streams (HoeffdingTree and HoeffdingAdaptiveTree) work as well as they do. In doing so, we identify thirteen unique unspecified design decisions in both the theoretical constructs and their implementations with substantial and consequential effects on predictive accuracy---design decisions that, without necessarily changing the essence of the algorithms, drive algorithm performance. We begin a larger conversation about explainability not just of the model but also of the processes responsible for an algorithm's success.