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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...

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Autores principales: Blockeel, Hendrik, Devos, Laurens, Frénay, Benoît, Nanfack, Géraldin, Nijssen, Siegfried
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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.
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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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