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Decision trees: from efficient prediction to responsible AI
This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which the research is situated, and summarizes strengths and weakn...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411911/ https://www.ncbi.nlm.nih.gov/pubmed/37565044 http://dx.doi.org/10.3389/frai.2023.1124553 |
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author | Blockeel, Hendrik Devos, Laurens Frénay, Benoît Nanfack, Géraldin Nijssen, Siegfried |
author_facet | Blockeel, Hendrik Devos, Laurens Frénay, Benoît Nanfack, Géraldin Nijssen, Siegfried |
author_sort | Blockeel, Hendrik |
collection | PubMed |
description | This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which the research is situated, and summarizes strengths and weaknesses of decision trees in this context. The main goal of the article is to clarify the broad relevance to machine learning and artificial intelligence, both practical and theoretical, that decision trees still have today. |
format | Online Article Text |
id | pubmed-10411911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104119112023-08-10 Decision trees: from efficient prediction to responsible AI Blockeel, Hendrik Devos, Laurens Frénay, Benoît Nanfack, Géraldin Nijssen, Siegfried Front Artif Intell Artificial Intelligence This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which the research is situated, and summarizes strengths and weaknesses of decision trees in this context. The main goal of the article is to clarify the broad relevance to machine learning and artificial intelligence, both practical and theoretical, that decision trees still have today. Frontiers Media S.A. 2023-07-26 /pmc/articles/PMC10411911/ /pubmed/37565044 http://dx.doi.org/10.3389/frai.2023.1124553 Text en Copyright © 2023 Blockeel, Devos, Frénay, Nanfack and Nijssen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Blockeel, Hendrik Devos, Laurens Frénay, Benoît Nanfack, Géraldin Nijssen, Siegfried Decision trees: from efficient prediction to responsible AI |
title | Decision trees: from efficient prediction to responsible AI |
title_full | Decision trees: from efficient prediction to responsible AI |
title_fullStr | Decision trees: from efficient prediction to responsible AI |
title_full_unstemmed | Decision trees: from efficient prediction to responsible AI |
title_short | Decision trees: from efficient prediction to responsible AI |
title_sort | decision trees: from efficient prediction to responsible ai |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411911/ https://www.ncbi.nlm.nih.gov/pubmed/37565044 http://dx.doi.org/10.3389/frai.2023.1124553 |
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