Visualizing Uncertainty in Predictive Models

  • Penny RheingansEmail author
  • Marie desJardins
  • Wallace Brown
  • Alex Morrow
  • Doug Stull
  • Kevin Winner
Part of the Mathematics and Visualization book series (MATHVISUAL)


Predictive models are used in many fields to characterize relationships between the attributes of an instance and its classification. While these models can provide valuable support to decision-making, they can be challenging to understand and evaluate. While they provide predicted classifications, they do not generally include indications of confidence in those predictions. Typical quality measures for predictive models are the percentage of predictions which are made correctly. These measures can give some insight into how often the model is correct, but provide little help in understanding under what conditions the model performs well (or poorly). We present a framework for improving understanding of predictive models based on the methods of both machine learning and data visualization. We demonstrate this framework on models that use attributes about individuals in a census data set to predict other attributes of those individuals.


True Class Input Attribute Dimension Reduction Method Dimension Reduction Technique Income Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This preliminary work was supported by NSF EAGER #1050168 and by NSF REU Supplement #1129683. Thanks to David Mann for his contribution to the project.


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Penny Rheingans
    • 1
    Email author
  • Marie desJardins
    • 1
  • Wallace Brown
    • 1
  • Alex Morrow
    • 1
  • Doug Stull
    • 1
  • Kevin Winner
    • 1
  1. 1.University of Maryland Baltimore CountyBaltimoreUSA

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