Melody：生成和可视化机器学习模型摘要，以同时理解数据和分类器
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together
摘要：
随着机器学习模型的日益复杂，开发仅关注一个实例（局部解释）的模型解释技术以确保对原始模型的忠实性的趋势日益增长。虽然这些技术提供了对各种数据基元（例如，表格、图像或文本），整体可解释人工智能（XAI）体验还需要对模型和数据集进行全局解释，以实现不同粒度的意义构建。因此，协同模型解释和可视化分析方法具有巨大的潜力。在本文中，我们提出了MELODY，一个交互式的算法来构建一个最佳的全球概述的模型和数据的行为，通过总结当地的解释，使用信息论。结果（即，解释概要）不需要额外的学习模型、数据原语的限制或来自用户的机器学习知识。我们还设计了MELODY UI，这是一个交互式视觉分析系统，用于演示解释摘要如何将各种XAI任务中的点从全局概述连接到本地检查。我们提出了三个使用场景的表格，图像和文本分类，以说明如何概括不同数据的模型可解释性。我们的实验表明，我们的方法：（1）提供了一个更好的解释总结相比，一个简单的信息理论总结和（2）实现了显着的加速在端到端的数据建模管道。
英文摘要：
With the increasing sophistication of machine learning models, there are growing trends of developing model explanation techniques that focus on only one instance (local explanation) to ensure faithfulness to the original model. While these techniques provide accurate model interpretability on various data primitive (e.g., tabular, image, or text), a holistic Explainable Artificial Intelligence (XAI) experience also requires a global explanation of the model and dataset to enable sensemaking in different granularity. Thus, there is a vast potential in synergizing the model explanation and visual analytics approaches. In this paper, we present MELODY, an interactive algorithm to construct an optimal global overview of the model and data behavior by summarizing the local explanations using information theory. The result (i.e., an explanation summary) does not require additional learning models, restrictions of data primitives, or the knowledge of machine learning from the users. We also design MELODY UI, an interactive visual analytics system to demonstrate how the explanation summary connects the dots in various XAI tasks from a global overview to local inspections. We present three usage scenarios regarding tabular, image, and text classifications to illustrate how to generalize model interpretability of different data. Our experiments show that our approaches: (1) provides a better explanation summary compared to a straightforward information-theoretic summarization and (2) achieves a significant speedup in the end-to-end data modeling pipeline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
