The machine learning model as a guide
Benedikt Grimmeisen, Andreas Theissler · 2020
The labeling of datasets is an important task for supervised and semi-supervised machine learning that can be addressed with visual analytics. With model-based active learning and user-based interactive labeling, there are two complementary strategies for this task. We present an approach that combines the strengths of both areas and aims to guide users through model-based recommendations and highlighting in one interface when selecting and labeling instances. For this purpose, an active learning strategy is used to recommend useful instances in addition to the user-based selection of instances. We have implemented the approach and conducted a user survey to research the effects guidance by visual cues has on the users' selection strategies. The proposed approach combines both perspectives in a single interactive visualization to support the user with different degrees of guidance in the selection of instances. Our results of the user survey suggest that user guidance has a positive influence on the users' perceived confidence and difficulty in selecting instances, on their orientation, and on their perceived impression of the models' performance. A video of the approach is available: https://youtu.be/TYPWG85Akn0.