Abstract Although Machine Learning (ML) tools and techniques are widespread and quite popular, in some cases, the patterns found by a model are either not accessible or not easily understandable from a human perspective. Therefore, not only model fitness given a quality metric is important, but also understanding how it makes decisions has become critical. One example of an ML approach growing in relevance that still lacks support to interpretation is the Learning to Rank (LtR). LtR models are typically used to rank elements, and, as in most ML areas, much effort has been put into creating more accurate models, but little or no effort has been devoted to understanding how elements are ranked. In this paper, we propose RankViz, a novel visualization framework that aims to fill this gap by supporting LtR model analysis and interpretation through a set of coordinated visualizations. RankViz provides information about the most important data features to a specific ranking result, supports a detailed comparative analysis of elements’ positions, and enables the investigation of iterative models evolution. Our results and study cases show the usefulness of the proposed framework to investigate a model and to aid users in creating and understanding a ranking, supporting tasks that are difficult, if not impossible, to be executed without proper visual representations.

中文翻译：

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RankViz：辅助解释学习排名算法的可视化框架

摘要 尽管机器学习 (ML) 工具和技术非常普遍且非常流行，但在某些情况下，从人类的角度来看，模型发现的模式要么无法访问，要么不易理解。因此，不仅给定质量指标的模型适应度很重要，而且了解它如何做出决策也变得至关重要。ML 方法相关性不断增长但仍缺乏对解释的支持的一个例子是 Learning to Rank (LtR)。LtR 模型通常用于对元素进行排序，并且与大多数 ML 领域一样，已经投入了大量精力来创建更准确的模型，但很少或根本没有致力于了解元素如何排序。在本文中，我们提出 RankViz，一种新颖的可视化框架，旨在通过一组协调的可视化支持 LtR 模型分析和解释来填补这一空白。RankViz 为特定排名结果提供有关最重要数据特征的信息，支持对元素位置的详细比较分析，并支持对迭代模型演化的调查。我们的结果和研究案例显示了所提出的框架在研究模型和帮助用户创建和理解排名方面的有用性，支持在没有适当视觉表示的情况下难以执行的任务。并能够研究迭代模型的演变。我们的结果和研究案例显示了所提出的框架在研究模型和帮助用户创建和理解排名方面的有用性，支持在没有适当视觉表示的情况下难以执行的任务。并能够研究迭代模型的演变。我们的结果和研究案例显示了所提出的框架在研究模型和帮助用户创建和理解排名方面的有用性，支持在没有适当视觉表示的情况下难以执行的任务。