MLMC: Visualizing Multi-Label Classification
Torsten Möller
University of Vienna
Machine learning classifiers are increasingly applied to complex tasks such as audio tagging, image labeling, and text classification – many of which require multi-label classification. Traditional evaluation tools, often limited to single metrics such as accuracy, fall short of providing insight into classifier behavior across multiple labels. To address this, we designed MLMC, an interactive visualization tool for evaluating and comparing multi-label classifiers. Based on expert interviews, MLMC supports analysis at instance-, label-, and classifier-level views, offering a scalable, more interpretable alternative. I demonstrate its use across three different domains and describe its core algorithms and user interface. Two pilot studies (N= each) provided insight into MLMC’s usability and showed improved task accuracy, consistency, and user confidence compared to confusion matrices. Results highlight MLMC’s potential as a practical tool for intuitive evaluation of multi-label classifiers, with implications for a broad range of machine learning applications.
Keywords: Classification, Multi-Label, Visualization Design Study. (Use at most 3 keywords)