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Knowledge and Information Systems

, Volume 41, Issue 3, pp 647–665 | Cite as

Explaining prediction models and individual predictions with feature contributions

  • Erik ŠtrumbeljEmail author
  • Igor Kononenko
Regular Paper

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.

Keywords

Knowledge discovery Data mining Visualization  Interpretability  Decision support 

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

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