Permutation-based Hypothesis Testing for Neural Networks

Neural networks are powerful predictive models, but they provide little insight into the nature of relationships between predictors and outcomes. Although numerous methods have been proposed to quantify the relative contributions of input features, statistical inference and hypothesis testing of fea...

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Bibliographic Details
Published in:arXiv.org 2023-01
Main Authors: Mandel, Francesca, Barnett, Ian
Format: Article
Language:English
Subjects:
Computer architecture
Hypotheses
Neural networks
Permutations
Prediction models
Statistical inference
Online Access:Get full text
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Summary:Neural networks are powerful predictive models, but they provide little insight into the nature of relationships between predictors and outcomes. Although numerous methods have been proposed to quantify the relative contributions of input features, statistical inference and hypothesis testing of feature associations remain largely unexplored. We propose a permutation-based approach to testing that uses the partial derivatives of the network output with respect to specific inputs to assess both the significance of input features and whether significant features are linearly associated with the network output. These tests, which can be flexibly applied to a variety of network architectures, enhance the explanatory power of neural networks, and combined with powerful predictive capability, extend the applicability of these models.
ISSN:2331-8422