Emergent and Unspecified Behaviors in Streaming Decision Trees
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Detecting and Summarizing Emergent Events in Microblogs and Social Media Streams by Dynamic Centralities
Methods for detecting and summarizing emergent keywords have been extensively studied since social media and microblogging activities have started to play an important role in data analysis and decision making. We presen…
Decision MakingSimplest Streaming Trees
Decision forests, including random forests and gradient boosting trees, remain the leading machine learning methods for many real-world data problems, especially on tabular data. However, most of the current implementati…
Continual LearningTransfer LearningVHT: Vertical Hoeffding Tree
IoT Big Data requires new machine learning methods able to scale to large size of data arriving at high speed. Decision trees are popular machine learning models since they are very effective, yet easy to interpret and v…
BIG-bench Machine LearningConfidence Decision Trees via Online and Active Learning for Streaming (BIG) Data
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence intervals to estimate the gain associated with each split leads to very effective methods, like the popular Hoeffding tree alg…
Active LearningMIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
Streaming decision trees are natural candidates for open-world continual learning, as they perform local updates, enjoy bounded memory, and static decision boundaries. Despite these, they still fail in online class-incre…
class-incremental learningContinual Learning