Decision trees: from efficient prediction to responsible AI

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

Résultats de recherche: Contribution à un journal/une revueArticle de revueRevue par des pairs

4 Téléchargements (Pure)

Résumé

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.

langue originaleAnglais
Numéro d'article1124553
journalFrontiers in Artificial Intelligence
Volume6
Les DOIs
Etat de la publicationPublié - 2023

Empreinte digitale

Examiner les sujets de recherche de « Decision trees: from efficient prediction to responsible AI ». Ensemble, ils forment une empreinte digitale unique.

Contient cette citation