Visualizing the function computed by a feedforward neural network


Authors: Tony A. Plate, Joel Bert, John Grace, Pierre Band
Source: GZipped PostScript (495kb); Adobe PDF (900kb)

A method for visualizing the function computed by a feedforward neural network is presented. It is most suitable for models with continuous inputs and a small number of outputs, where the output function is reasonably smooth, as in regression or probabilistic classification tasks. The visualization makes readily apparent the effects of each input and the way in which the functions deviates from a linear function. The visualization can also assist in identifying interactions in the fitted model. The method uses only the input-output relationship and thus can be applied to any predictive statistical model, including bagged and committee models, which are otherwise difficult to interpret. The visualization method is demonstrated on a neural-network model of how the risk of lung cancer is affected by smoking and drinking.

Note: Pages 10 and 11 of this report contain color plots. They will come out in color on a color postscript printer.

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