提出可解释的可视化方法，看清分类模型如何区分数据边界。
Interpretable Visualizations of Data Spaces for Classification Problems
- 结合监督与无监督学习，生成可解释的数据空间映射图。
- 在化学神经毒性分类任务中成功可视化决策边界。
- 适合需要理解模型决策逻辑的研究者使用。
分类模型如何‘看’我们的数据？尽管它们在区分行为方面表现优异，但现有可视化技术难以揭示其决策视角。本文提出一种混合式有监督-无监督方法，专门用于可视化分类问题的决策边界。该方法生成的人类可理解地图可进行定性与定量分析，我们在化学神经毒性分类任务中验证了其有效性。尽管以化学问题为背景，该方法可推广至其他领域，帮助‘揭开’机器学习分类模型的运作机制。
原文摘要 · Abstract (English)
How do classification models "see" our data? Based on their success in delineating behaviors, there must be some lens through which it is easy to see the boundary between classes; however, our current set of visualization techniques makes this prospect difficult. In this work, we propose a hybrid supervised-unsupervised technique distinctly suited to visualizing the decision boundaries determined by classification problems. This method provides a human-interpretable map that can be analyzed qualitatively and quantitatively, which we demonstrate through visualizing and interpreting a decision boundary for chemical neurotoxicity. While we discuss this method in the context of chemistry-driven problems, its application can be generalized across subfields for "unboxing" the operations of machine-learning classification models.
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