Decision trees: from efficient prediction to responsible AI

Hendrik Blockeel; Laurens Devos; Benoît Frénay; Géraldin Nanfack; Siegfried Nijssen

doi:10.3389/frai.2023.1124553

Decision trees: from efficient prediction to responsible AI

Hendrik Blockeel, Laurens Devos, Benoît Frénay, Géraldin Nanfack, Siegfried Nijssen

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Abstract

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.

Original language	English
Article number	1124553
Journal	Frontiers in Artificial Intelligence
Volume	6
DOIs	https://doi.org/10.3389/frai.2023.1124553
Publication status	Published - 2023

Keywords

combinatorial optimization
decision trees
ensembles
explainable AI
learning under constraints
machine learning
responsible AI

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10.3389/frai.2023.1124553

frai-06-1124553Final published version, 528 KBLicence: CC BY

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title = "Decision trees: from efficient prediction to responsible AI",

abstract = "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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AU - Frénay, Benoît

AU - Nanfack, Géraldin

AU - Nijssen, Siegfried

PY - 2023

Y1 - 2023

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AB - 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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KW - decision trees

KW - ensembles

KW - explainable AI

KW - learning under constraints

KW - machine learning

KW - responsible AI

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JO - Frontiers in Artificial Intelligence

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Decision trees: from efficient prediction to responsible AI

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Keywords

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