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Visualizing high-dimensional predictive model quality
Rheingans, P.; DesJardins, M.;
Visualization 2000. Proceedings
13-13 Oct. 2000
Page(s):493
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496
Abstract:
Using inductive learning techniques to construct classification models from large, high-dimensional data sets is a useful way to make predictions in complex domains. However, these models can be difficult for users to understand. We have developed a set of visualization methods that help users to understand and analyze the behavior of learned models, including techniques for high-dimensional data space projection, display of probabilistic predictions, variable/class correlation, and instance mapping. We show the results of applying these techniques to models constructed from a benchmark data set of census data, and draw conclusions about the utility of these methods for model understanding.
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