论文标题
流式决策树中的新兴行为和未指定的行为
Emergent and Unspecified Behaviors in Streaming Decision Trees
论文作者
论文摘要
Hoeffding树是用于不断发展的数据流的决策树学习中的最新方法。这些非常快速的决策树是在许多实际应用中使用的，在许多实际应用中，由于其效率而实时创建数据。在这项工作中，我们解释了这些流式决策树算法的解释，这些算法（Hoeffdingtree和Hoeffdingadaptivetree）以及它们都可以做到这一点。在此过程中，我们确定了13个独特的未指定设计决策及其实现，对预测准确性产生了重大和结果的影响 - 设计决策，而不必改变算法的本质，而是驱动算法性能。我们开始就模型的解释性，而是有关算法成功的过程的更大对话。
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.
