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Model Interpretability: Advances in Interpretable Machine Learning

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Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

Abstract

This chapter expands on intrinsic model interpretability discussed in the last chapter to include many modern techniques that are both interpretable and accurate on many real-world problems. The chapter starts with differentiating between interpretable and explainable models and why, in specific domains where high stakes decisions need to be made, interpretable models should be a natural choice. The chapter covers some state-of-the-art interpretable models that are ensemble-based, decision tree-based, rules-based, and scoring system based. We describe each algorithm in sufficient detail and then use the diabetes classification or insurance claims regression dataset to practically demonstrate the output of each, along with interpretations and observations.

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Authors and Affiliations

  1. Ashburn, VA, USA

    Uday Kamath

  2. Nashville, TN, USA

    John Liu

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  1. Uday Kamath
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  2. John Liu
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Kamath, U., Liu, J. (2021). Model Interpretability: Advances in Interpretable Machine Learning. In: Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-83356-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-83356-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-83355-8

  • Online ISBN: 978-3-030-83356-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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