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Interpretable Quantile Regression by Optimal Decision Trees

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Advances in Intelligent Data Analysis XXII (IDA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14642))

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Abstract

The field of machine learning is subject to an increasing interest in models that are not only accurate but also interpretable and robust, thus allowing their end users to understand and trust AI systems. This paper presents a novel method for learning a set of optimal quantile regression trees. The advantages of this method are that (1) it provides predictions about the complete conditional distribution of a target variable without prior assumptions on this distribution; (2) it provides predictions that are interpretable; (3) it learns a set of optimal quantile regression trees without compromising algorithmic efficiency compared to learning a single tree.

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Notes

  1. 1.

    Source code is available at https://github.com/valentinlemaire/pydl8.5.

  2. 2.

    We however recommend setting the number of quantiles to be lower than the minimum support in each leaf to avoid skewed estimations.

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Author information

Authors and Affiliations

  1. Euranova, Rue Emile Francqui, Mont-Saint-Guibert, Belgium

    Valentin Lemaire

  2. Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    Gaël Aglin & Siegfried Nijssen

Authors
  1. Valentin Lemaire
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  2. Gaël Aglin
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  3. Siegfried Nijssen
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Corresponding author

Correspondence to Valentin Lemaire .

Editor information

Editors and Affiliations

  1. Stockholm University, Kista, Sweden

    Ioanna Miliou

  2. Fraunhofer IAIS, Sankt Augustin, Germany

    Nico Piatkowski

  3. Stockholm University, Kista, Sweden

    Panagiotis Papapetrou

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Lemaire, V., Aglin, G., Nijssen, S. (2024). Interpretable Quantile Regression by Optimal Decision Trees. In: Miliou, I., Piatkowski, N., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol 14642. Springer, Cham. https://doi.org/10.1007/978-3-031-58553-1_17

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  • DOI: https://doi.org/10.1007/978-3-031-58553-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-58555-5

  • Online ISBN: 978-3-031-58553-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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