ATMSeer：提高自动化机器学习的透明度和可控性
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
摘要：
为了减轻手动选择机器学习算法和调整超参数的痛苦，人们开发了自动机器学习（AutoML）方法来自动搜索良好的模型。由于巨大的模型搜索空间，不可能尝试所有模型。用户倾向于不信任自动结果，并尽可能地增加搜索预算，从而破坏了AutoML的效率。为了解决这些问题，我们设计并实现了ATMSeer，一个交互式可视化工具，支持用户在细化AutoML的搜索空间和分析结果。为了指导ATMSeer的设计，我们根据对机器学习专家的采访，推导出使用AutoML的工作流程。提出了一种多粒度可视化，使用户能够监视AutoML过程，分析搜索的模型，并在实时的细化搜索空间。我们通过两个案例研究，专家访谈和13个最终用户的用户研究证明了ATMSeer的实用性和可用性。
英文摘要：
To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.
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。
