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Conference Papers Year : 2018

Measures of Model Interpretability for Model Selection

André Carrington
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  • PersonId : 1043707
Paul Fieguth
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Abstract

The literature lacks definitions for quantitative measures of model interpretability for automatic model selection to achieve high accuracy and interpretability, hence we define inherent model interpretability. We extend the work of Lipton et al. and Liu et al. from qualitative and subjective concepts of model interpretability to objective criteria and quantitative measures. We also develop another new measure called simplicity of sensitivity and illustrate prior, initial and posterior measurement. Measures are tested and validated with some measures recommended for use. It is demonstrated that high accuracy and high interpretability are jointly achievable with little to no sacrifice in either.
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hal-02060060 , version 1 (07-03-2019)

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André Carrington, Paul Fieguth, Helen Chen. Measures of Model Interpretability for Model Selection. 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2018, Hamburg, Germany. pp.329-349, ⟨10.1007/978-3-319-99740-7_24⟩. ⟨hal-02060060⟩
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