A decision-theoretic approach for model interpretability in Bayesian framework

Homayun Afrabandpey*, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)
162 Downloads (Pure)

Abstract

A salient approach to interpretable machine learning is to restrict modeling to simple models. In the Bayesian framework, this can be pursued by restricting the model structure and prior to favor interpretable models. Fundamentally, however, interpretability is about users’ preferences, not the data generation mechanism; it is more natural to formulate interpretability as a utility function. In this work, we propose an interpretability utility, which explicates the trade-off between explanation fidelity and interpretability in the Bayesian framework. The method consists of two steps. First, a reference model, possibly a black-box Bayesian predictive model which does not compromise accuracy, is fitted to the training data. Second, a proxy model from an interpretable model family that best mimics the predictive behaviour of the reference model is found by optimizing the interpretability utility function. The approach is model agnostic—neither the interpretable model nor the reference model are restricted to a certain class of models—and the optimization problem can be solved using standard tools. Through experiments on real-word data sets, using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the alternative of restricting the prior. We also propose a systematic way to measure stability of interpretabile models constructed by different interpretability approaches and show that our proposed approach generates more stable models.

Original languageEnglish
Pages (from-to)1855-1876
Number of pages22
JournalMachine Learning
Volume109
Issue number9-10
DOIs
Publication statusPublished - 1 Sept 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Bayesian predictive models
  • Interpretable machine learning

Fingerprint

Dive into the research topics of 'A decision-theoretic approach for model interpretability in Bayesian framework'. Together they form a unique fingerprint.
  • White-boxed artificial intelligence

    Kaski, S. (Principal investigator), Peltola, T. (Project Member), Daee, P. (Project Member) & Celikok, M. M. (Project Member)

    01/01/201831/12/2019

    Project: Academy of Finland: Other research funding

Cite this