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KDD
2004
ACM

Discovering additive structure in black box functions

14 years 12 months ago
Discovering additive structure in black box functions
Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that drive it. A common approach to interpretation is to plot the dependence of a learned function on one or two predictors. We present a method that seeks not to display the behavior of a function, but to evaluate the importance of nonadditive interactions within any set of variables. Should the function be close to a sum of low dimensional components, these components can be viewed and even modeled parametrically. Alternatively, the work here provides an indication of where intrinsically high-dimensional behavior takes place. The calculations used in this paper correspond closely with the functional ANOVA decomposition; a well-developed construction in Statistics. In particular, the proposed score of interaction importance measures the loss associated with the projection of the prediction function onto a space of ad...
Giles Hooker
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2004
Where KDD
Authors Giles Hooker
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