A4 Refereed article in a conference publication
VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models
Authors: He Chen, Raj Vishnu, Moen Hans, Gröhn Tommi, Wang Chen, Peltonen Laura-Maria, Koivusalo Saila, Marttinen Pekka, Jacucci Giulio
Conference name: International Conference on Intelligent User Interfaces
Publisher: Association for Computing Machinery
Publishing place: New York
Publication year: 2024
Journal: International Conference on Intelligent User Interfaces
Book title : Proceedings of the 29th International Conference on Intelligent User Interfaces
First page : 229
Last page: 244
ISBN: 979-8-4007-0508-3
DOI: https://doi.org/10.1145/3640543.3645151
Web address : https://doi.org/10.1145/3640543.3645151
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/387604395
To compare and select machine learning models, relying on performance measures alone may not always be sufficient. This is particularly the case where different subsets, features, and predicted results may vary in importance relative to the task at hand. Explanation and visualization techniques are required to support model sensemaking and informed decision-making. However, a review shows that existing systems are mostly designed for model developers and not evaluated with target users in their effectiveness. To address this issue, this research proposes an interactive visualization, VMS (Visualization for Model Sensemaking and Selection), for users of the model to compare and select predictive models. VMS integrates performance-, instance-, and feature-level analysis to evaluate models from multiple angles. Particularly, a feature view integrating the value and contribution of hundreds of features supports model comparison on local and global scales. We exemplified VMS for comparing models predicting patients’ hospital length of stay through time-series health records and evaluated the prototype with 16 participants from the medical field. Results reveal evidence that VMS supports users to rationalize models in multiple ways and enables users to select the optimal models with a small sample size. User feedback suggests future directions on incorporating domain knowledge in model training, such as for different patient groups considering different sets of features as important.
Downloadable publication This is an electronic reprint of the original article. |  |
