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Explaining prediction models and individual predictions with feature contributions

Explaining prediction models and individual predictions with feature contributions We present a sensitivity analysis-based method for explaining prediction models that can be applied to any type of classification or regression model. Its advantage over existing general methods is that all subsets of input features are perturbed, so interactions and redundancies between features are taken into account. Furthermore, when explaining an additive model, the method is equivalent to commonly used additive model-specific methods. We illustrate the method’s usefulness with examples from artificial and real-world data sets and an empirical analysis of running times. Results from a controlled experiment with 122 participants suggest that the method’s explanations improved the participants’ understanding of the model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge and Information Systems Springer Journals

Explaining prediction models and individual predictions with feature contributions

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References (53)

Publisher
Springer Journals
Copyright
Copyright © 2013 by Springer-Verlag London
Subject
Computer Science; Information Systems and Communication Service; Business Information Systems
ISSN
0219-1377
eISSN
0219-3116
DOI
10.1007/s10115-013-0679-x
Publisher site
See Article on Publisher Site

Abstract

We present a sensitivity analysis-based method for explaining prediction models that can be applied to any type of classification or regression model. Its advantage over existing general methods is that all subsets of input features are perturbed, so interactions and redundancies between features are taken into account. Furthermore, when explaining an additive model, the method is equivalent to commonly used additive model-specific methods. We illustrate the method’s usefulness with examples from artificial and real-world data sets and an empirical analysis of running times. Results from a controlled experiment with 122 participants suggest that the method’s explanations improved the participants’ understanding of the model.

Journal

Knowledge and Information SystemsSpringer Journals

Published: Aug 30, 2013

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