Lessons on interpretable machine learning from particle physics

Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods...

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Bibliographic Details
Published in:Nature reviews physics Vol. 4; no. 5; pp. 284 - 286
Main Authors: Grojean, Christophe, Paul, Ayan, Qian, Zhuoni, Strümke, Inga
Format: Journal Article
Language:English
Published: London Nature Publishing Group 01.05.2022
Subjects:
Algorithms
Artificial intelligence
Decision trees
Kinematics
Machine learning
Neural networks
Particle physics
Variables
ISSN:2522-5820
Online Access:Get full text
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Summary:Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods commonly used in particle physics.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Commentary-1
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ISSN:2522-5820
DOI:10.1038/s42254-022-00456-0