Decision trees: from efficient prediction to responsible AI

Frontiers in Artificial Intelligence

Artificial Intelligence

Laurens Devos¹  Hendrik Blockeel¹  Benoît Frénay²  Géraldin Nanfack²  Siegfried Nijssen³ 

[1] Department of Computer Science, KU Leuven, Leuven, Belgium;Institute for Artificial Intelligence (Leuven.AI), KU Leuven, Leuven, Belgium;Faculty of Computer Science, Université de Namur, Namur, Belgium;ICTEAM, UCLouvain, Ottignies-Louvain-la-Neuve, Belgium;

关键词: decision trees;    ensembles;    responsible AI;    machine learning;    learning under constraints;    explainable AI;    combinatorial optimization;   

DOI  :  10.3389/frai.2023.1124553

 received in 2022-12-15, accepted in 2023-07-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Blockeel, Devos, Frénay, Nanfack and Nijssen.