PipelineProfiler：一种用于探索AutoML管道的可视化分析工具
PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines
摘要：
近年来，已经提出了各种各样的自动机器学习（AutoML）方法来搜索和生成端到端学习管道。虽然这些技术有助于为现实世界的应用程序创建模型，但由于其黑盒性质，底层算法的复杂性以及它们派生的大量管道，开发人员很难调试这些系统。对于机器学习专家来说，选择一个非常适合给定问题或一类问题的AutoML系统也是一个挑战。在本文中，我们提出了PipelineProfiler，一个交互式可视化工具，允许探索和比较的解决方案空间的机器学习（ML）管道AutoML系统。PipelineProfiler集成了Xueyter Notebook，可以与常见的数据科学工具一起使用，以实现对ML管道的丰富分析，并提供有关生成它们的算法的见解。我们通过几个使用案例展示了我们的工具的实用性，其中PipelineProfiler用于更好地理解和改进真实世界的AutoML系统。此外，我们通过对六位开发和评估AutoML工具的数据科学家的有声思维实验进行详细分析来验证我们的方法。
英文摘要：
In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines. While these techniques facilitate the creation of models for real-world applications, given their black-box nature, the complexity of the underlying algorithms, and the large number of pipelines they derive, it is difficult for their developers to debug these systems. It is also challenging for machine learning experts to select an AutoML system that is well suited for a given problem or class of problems. In this paper, we present the PipelineProfiler, an interactive visualization tool that allows the exploration and comparison of the solution space of machine learning (ML) pipelines produced by AutoML systems. PipelineProfiler is integrated with Jupyter Notebook and can be used together with common data science tools to enable a rich set of analyses of the ML pipelines and provide insights about the algorithms that generated them. We demonstrate the utility of our tool through several use cases where PipelineProfiler is used to better understand and improve a real-world AutoML system. Furthermore, we validate our approach by presenting a detailed analysis of a think-aloud experiment with six data scientists who develop and evaluate AutoML tools.
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。
