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Autori principali: Karolczak, Jacek, Stefanowski, Jerzy
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2405.21036
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author Karolczak, Jacek
Stefanowski, Jerzy
author_facet Karolczak, Jacek
Stefanowski, Jerzy
contents The need for interpreting machine learning models is addressed through prototype explanations within the context of tree ensembles. An algorithm named Adaptive Prototype Explanations of Tree Ensembles (A-PETE) is proposed to automatise the selection of prototypes for these classifiers. Its unique characteristics is using a specialised distance measure and a modified k-medoid approach. Experiments demonstrated its competitive predictive accuracy with respect to earlier explanation algorithms. It also provides a a sufficient number of prototypes for the purpose of interpreting the random forest classifier.
format Preprint
id arxiv_https___arxiv_org_abs_2405_21036
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A-PETE: Adaptive Prototype Explanations of Tree Ensembles
Karolczak, Jacek
Stefanowski, Jerzy
Machine Learning
The need for interpreting machine learning models is addressed through prototype explanations within the context of tree ensembles. An algorithm named Adaptive Prototype Explanations of Tree Ensembles (A-PETE) is proposed to automatise the selection of prototypes for these classifiers. Its unique characteristics is using a specialised distance measure and a modified k-medoid approach. Experiments demonstrated its competitive predictive accuracy with respect to earlier explanation algorithms. It also provides a a sufficient number of prototypes for the purpose of interpreting the random forest classifier.
title A-PETE: Adaptive Prototype Explanations of Tree Ensembles
topic Machine Learning
url https://arxiv.org/abs/2405.21036