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Interpretation of Black-Box Predictive Models

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Abstract

Many machine learning applications involve predictive data-analytic modeling using black-box techniques. A common problem in such studies is understanding/interpretation of estimated nonlinear high-dimensional models. Whereas human users naturally favor simple interpretable models, such models may not be practically feasible with modern adaptive methods such as Support Vector Machines (SVMs) , Multilayer Perceptron Networks (MLPs), AdaBoost , etc. This chapter provides a brief survey of the current techniques for visualization and interpretation of SVM-based classification models, and then highlights potential problems with such methods. We argue that, under the VC-theoretical framework, model interpretation cannot be achieved via technical analysis of predictive data-analytic models. That is, any meaningful interpretation should incorporate application domain knowledge outside data analysis. We also describe a simple graphical technique for visualization of SVM classification models.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Vladimir Cherkassky

  2. Research and Technology Center, Robert Bosch LLC, Palo Alto, CA, 94304, USA

    Sauptik Dhar

Authors
  1. Vladimir Cherkassky
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  2. Sauptik Dhar
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Corresponding author

Correspondence to Vladimir Cherkassky .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Royal Holloway, Univ of London, Egham, Surrey, United Kingdom

    Vladimir Vovk

  2. Frederick University, Nicosia, Cyprus

    Harris Papadopoulos

  3. Dept. of Computer Science, University of London, Egham, Surrey, United Kingdom

    Alexander Gammerman

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Cherkassky, V., Dhar, S. (2015). Interpretation of Black-Box Predictive Models. In: Vovk, V., Papadopoulos, H., Gammerman, A. (eds) Measures of Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-21852-6_19

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  • DOI: https://doi.org/10.1007/978-3-319-21852-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21851-9

  • Online ISBN: 978-3-319-21852-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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