Visualizing High-Dimensional Predictive Model Quality

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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Cite this paper

@inproceedings{Rheingans2000VisualizingHP, title={Visualizing High-Dimensional Predictive Model Quality}, author={Penny Rheingans and Marie desJardins}, year={2000} }