VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models

Chen He, Vishnu Raj, Hans Moen, Tommi Gröhn, Chen Wang, Laura Maria Peltonen, Saila Koivusalo, Pekka Marttinen, Giulio Jacucci

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2024 29th Annual Conference on Intelligent User Interfaces, IUI 2024
PublisherACM
Pages229-244
Number of pages16
ISBN (Electronic)9798400705083
DOIs
Publication statusPublished - 18 Mar 2024
MoE publication typeA4 Conference publication
EventInternational Conference on Intelligent User Interfaces - Greenville, United States
Duration: 18 Mar 202421 Mar 2024
Conference number: 29

Conference

ConferenceInternational Conference on Intelligent User Interfaces
Abbreviated titleIUI
Country/TerritoryUnited States
CityGreenville
Period18/03/202421/03/2024

Keywords

  • interactive machine learning
  • MIMIC-IV
  • XAI

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