Researchers from MIT and elsewhere have developed an interactive tool that, for the first time, lets users see and control how automated machine-learning systems work. The aim is to build confidence in these systems and find ways to improve them. Designing a machine-learning model for a certain task -- such as image classification, disease diagnoses, and stock market prediction -- is an arduous, time-consuming process. Experts first choose from among many different algorithms to build the model around. Then, they manually tweak "hyperparameters" -- which determine the model's overall structure -- before the model starts training.

Researchers from MIT and elsewhere have developed an interactive tool that, for the first time, lets users see and control how automated machine-learning systems work. The aim is to build confidence in these systems and find ways to improve them. Designing a machine-learning model for a certain task -- such as image classification, disease diagnoses, and stock market prediction -- is an arduous, time-consuming process. Experts first choose from among many different algorithms to build the model around. Then, they manually tweak "hyperparameters" -- which determine the model's overall structure -- before the model starts training.

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.