Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree

Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree

The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. Tree ensemble methods, such as Random Forests or XgBoost, are powerful learning tools for classification tasks. However, while combining mul...

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Bibliographic Details
Main Authors: Giulia Di Teodoro, Marta Monaci, Laura Palagi
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:EURO Journal on Computational Optimization
Subjects:
Tree ensembles
Visualizing forests
Optimal trees
Mixed-Integer Programming
Machine Learning
Interpretability
Online Access:http://www.sciencedirect.com/science/article/pii/S2192440624000017

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http://www.sciencedirect.com/science/article/pii/S2192440624000017

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