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...

Full description

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
Tags:	Add Tag No Tags, Be the first to tag this record!