提出VAR可视化工具，横向比较相似性能模型的结构差异。
VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performance
- 用热力图与散点图组合实现多模型横向对比
- 帮助开发者在特定条件下选出最优模型
- 适合关注模型可解释性与选择策略的研究者
在机器学习领域，对性能相近模型在特定条件下的评估长期受到关注。Rashomon集是指一组性能相近但结构各异的机器学习模型。传统分析侧重于垂直结构对比，即在同一模型内部不同层级特征的比较。然而，针对具有特定特征的多个模型之间的横向比较，缺乏有效的可视化方法。本文提出VAR可视化方案，通过热力图与散点图结合，实现对Rashomon集中模型的直观对比。借助VAR，机器学习开发者能够在特定应用场景下识别出最优模型，并更深入理解该集合的整体特性。
原文摘要 · Abstract (English)
Evaluating the performance of closely matched machine learning(ML) models under specific conditions has long been a focus of researchers in the field of machine learning. The Rashomon set is a collection of closely matched ML models, encompassing a wide range of models with similar accuracies but different structures. Traditionally, the analysis of these sets has focused on vertical structural analysis, which involves comparing the corresponding features at various levels within the ML models. However, there has been a lack of effective visualization methods for horizontally comparing multiple models with specific features. We propose the VAR visualization solution. VAR uses visualization to perform comparisons of ML models within the Rashomon set. This solution combines heatmaps and scatter plots to facilitate the comparison. With the help of VAR, ML model developers can identify the optimal model under specific conditions and better understand the Rashomon set's overall characteristics.
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