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.
@article{arxiv.1902.05009,
title = {ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning},
author = {Qianwen Wang and Yao Ming and Zhihua Jin and Qiaomu Shen and Dongyu Liu and Micah J. Smith and Kalyan Veeramachaneni and Huamin Qu},
journal= {arXiv preprint arXiv:1902.05009},
year = {2020}
}
Comments
Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland UK