Emergent and Unspecified Behaviors in Streaming Decision Trees

TL;DR

This paper investigates the underlying reasons for the effectiveness of streaming decision trees like HoeffdingTree and HoeffdingAdaptiveTree, revealing thirteen key design decisions that impact their accuracy and promoting explainability of these algorithms.

Contribution

It identifies thirteen critical, previously unspecified design decisions in streaming decision trees that significantly influence their performance and understanding.

Findings

Thirteen key design decisions affect algorithm accuracy.

Explanation of why streaming decision trees perform well.

Promotion of explainability in algorithm design and success.

Abstract

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.