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| Photo: Chelsea Turner, MIT |
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.
Recently developed automated machine-learning (AutoML) systems iteratively test and modify algorithms and those hyperparameters, and select the best-suited models. But the systems operate as “black boxes,” meaning their selection techniques are hidden from users. Therefore, users may not trust the results and can find it difficult to tailor the systems to their search needs.
In a paper presented at the ACM CHI Conference on Human Factors in Computing Systems, researchers from MIT, the Hong Kong University of Science and Technology (HKUST), and Zhejiang University describe a tool that puts the analyses and control of AutoML methods into users’ hands...
Case studies with machine-learning experts, who had no AutoML experience, revealed that user control does help improve the performance and efficiency of AutoML selection. User studies with 13 graduate students in diverse scientific fields — such as biology and finance — were also revealing. Results indicate three major factors — number of algorithms searched, system runtime, and finding the top-performing model — determined how users customized their AutoML searches. That information can be used to tailor the systems to users, the researchers say.
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Source: MIT News
