@conference {13482,
	title = {Visualizing high-dimensional predictive model quality},
	booktitle = {Visualization 2000. Proceedings},
	year = {2000},
	month = {2000///},
	pages = {493-496, - 493-496,},
	publisher = {IEEE},
	organization = {IEEE},
	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.},
	isbn = {0-7803-6478-3},
	doi = {10.1109/VISUAL.2000.885740},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=885740},
	author = {Rheingans,P. and desJardins, Marie}
}
