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Assessing the Stability of Interpretable Models

Riccardo Guidotti,S. Ruggieri

Published 2018 in arXiv.org

ABSTRACT

Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    arXiv.org

  • Publication date

    2018-10-22

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1810.09352
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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CONCEPTS

  • No concepts are published for this paper.

REFERENCES

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