Model-agnostic variable importance for predictive uncertainty: an entropy-based approach

Author:

Wood Danny,Papamarkou TheodoreORCID,Benatan Matt,Allmendinger Richard

Abstract

AbstractIn order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only the reasons for the predictions themselves, but also the reasons for the model’s level of confidence in those predictions. In this paper, we show how existing methods in explainability can be extended to uncertainty-aware models and how such extensions can be used to understand the sources of uncertainty in a model’s predictive distribution. In particular, by adapting permutation feature importance, partial dependence plots, and individual conditional expectation plots, we demonstrate that novel insights into model behaviour may be obtained and that these methods can be used to measure the impact of features on both the entropy of the predictive distribution and the log-likelihood of the ground truth labels under that distribution. With experiments using both synthetic and real-world data, we demonstrate the utility of these approaches to understand both the sources of uncertainty and their impact on model performance.

Publisher

Springer Science and Business Media LLC

Reference37 articles.

1. Antoran J, Bhatt U, Adel T, et al (2021) Getting a CLUE: a method for explaining uncertainty estimates. In: International conference on learning representations

2. Blundell C, Cornebise J, Kavukcuoglu K, et al (2015) Weight uncertainty in neural networks. In: International conference on machine learning

3. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

4. Casalicchio G, Molnar C, Bischl B (2018) Visualizing the feature importance for black box models. In: Machine learning and knowledge discovery in databases: European conference, ECML PKDD, Springer, pp 655–670. https://doi.org/10.1007/978-3-030-10925-7_40

5. Chai LR (2018) Uncertainty estimation in Bayesian neural networks and links to interpretability. Master’s thesis, University of Cambridge

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3