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Example-Based Explanations of Random Forest Predictions
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-8382-0300
2024 (English)In: Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Proceedings, Springer Science and Business Media Deutschland GmbH , 2024, Vol. 14642, p. 185-196Conference paper, Published paper (Refereed)
Abstract [en]

A random forest prediction can be computed by the scalar product of the labels of the training examples and a set of weights that are determined by the leafs of the forest into which the test object falls; each prediction can hence be explained exactly by the set of training examples for which the weights are non-zero. The number of examples used in such explanations is shown to vary with the dimensionality of the training set and hyperparameters of the random forest algorithm. This means that the number of examples involved in each prediction can to some extent be controlled by varying these parameters. However, for settings that lead to a required predictive performance, the number of examples involved in each prediction may be unreasonably large, preventing the user from grasping the explanations. In order to provide more useful explanations, a modified prediction procedure is proposed, which includes only the top-weighted examples. An investigation on regression and classification tasks shows that the number of examples used in each explanation can be substantially reduced while maintaining, or even improving, predictive performance compared to the standard prediction procedure.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2024. Vol. 14642, p. 185-196
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 14642
Keywords [en]
Example-based explanations, Explainable machine learning, Random forests
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-346545DOI: 10.1007/978-3-031-58553-1_15Scopus ID: 2-s2.0-85192135326OAI: oai:DiVA.org:kth-346545DiVA, id: diva2:1858461
Conference
22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, Apr 24 2024 - Apr 26 2024
Note

QC 20240521

Part of ISBN 978-303158555-5

Available from: 2024-05-16 Created: 2024-05-16 Last updated: 2024-05-21Bibliographically approved

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Boström, Henrik

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Language
  • de-DE
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Output format
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