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决策树:从高效预测到负责任的人工智能

Decision trees: from efficient prediction to responsible AI.

作者信息

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

机构信息

Department of Computer Science, KU Leuven, Leuven, Belgium.

Institute for Artificial Intelligence (Leuven.AI), KU Leuven, Leuven, Belgium.

出版信息

Front Artif Intell. 2023 Jul 26;6:1124553. doi: 10.3389/frai.2023.1124553. eCollection 2023.

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.

摘要

本文对决策树在机器学习和数据科学领域近四十年的作用进行了全景式的介绍。它勾勒了多年来决策树研究的发展历程,描述了该研究所处的更广泛背景,并总结了在此背景下决策树的优缺点。本文的主要目的是阐明决策树在当今机器学习和人工智能领域在实践和理论方面仍然具有的广泛相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec85/10411911/0eec7b7b227f/frai-06-1124553-g0001.jpg

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