Context sight: model understanding and debugging via interpretable context

Jun Yuan, E. Bertini
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引用次数: 1

Abstract

Model interpretation is increasingly important for successful model development and deployment. In recent years, many explanation methods are introduced to help humans understand how a machine learning model makes a decision on a specific instance. Recent studies show that contextualizing an individual model decision within a set of relevant examples can improve the model understanding. However, there is a lack of systematic study on what factors are considered when generating and using the context examples to explain model predictions, and how context examples help with model understanding and debugging in practice. In this work, we first identify a taxonomy of context generation and summarization through literature review. We then present Context Sight, a visual analytics system that integrates customized context generation and multiple-level context summarization to assist context exploration and interpretation. We evaluate the usefulness of the system through a detailed use case. This work is an initial step for a set of systematic research on how contextualization can help data scientists and practitioners understand and diagnose model behaviors, based on which we will gain a better understanding of the usage of context.
上下文视图:通过可解释的上下文来理解和调试模型
模型解释对于成功的模型开发和部署越来越重要。近年来,引入了许多解释方法来帮助人类理解机器学习模型如何对特定实例做出决策。最近的研究表明,将单个模型决策置于一组相关示例中可以提高对模型的理解。然而,在生成和使用上下文示例来解释模型预测时考虑了哪些因素,以及上下文示例如何在实践中帮助模型理解和调试方面,缺乏系统的研究。在这项工作中,我们首先通过文献综述确定了上下文生成和摘要的分类。然后,我们介绍了Context Sight,这是一个视觉分析系统,它集成了定制的上下文生成和多级上下文摘要,以协助上下文探索和解释。我们通过详细的用例来评估系统的有用性。这项工作是一系列系统研究的第一步,这些研究是关于上下文化如何帮助数据科学家和从业者理解和诊断模型行为的,在此基础上,我们将更好地理解上下文的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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