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Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles

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Explainable Artificial Intelligence (xAI 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2154))

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Abstract

Understanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop interpretation challenge have explored the extraction of the decision structure underlying the model taking advantage of graph simplification and path emphasis. However, while these efforts enhance the visualisation experience, they may either result in a visually complex representation or compromise the interpretability of the original ensemble model. In addressing this challenge, especially in complex scenarios, we introduce the Decision Predicate Graph (DPG) as a model-specific tool to provide a global interpretation of the model. DPG is a graph structure that captures the tree-based ensemble model and learned dataset details, preserving the relations among features, logical decisions, and predictions towards emphasising insightful points. Leveraging well-known graph theory concepts, such as the notions of centrality and community, DPG offers additional quantitative insights into the model, complementing visualisation techniques, expanding the problem space descriptions, and offering diverse possibilities for extensions. Empirical experiments demonstrate the potential of DPG in addressing traditional benchmarks and complex classification scenarios.

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Notes

  1. 1.

    https://github.com/LeonardoArrighi/DPG.

  2. 2.

    https://github.com/LeonardoArrighi/DPG/datasets.

  3. 3.

    https://github.com/LeonardoArrighi/DPG.

  4. 4.

    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html.

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Disclosure of Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Author information

Authors and Affiliations

  1. Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy

    Leonardo Arrighi

  2. Department of Economics, Business, Mathematics and Statistics, University of Trieste, Trieste, Italy

    Luca Pennella

  3. LMU Munich, Munich, Germany

    Gabriel Marques Tavares

  4. Munich Center for Machine Learning (MCML), Munich, Germany

    Gabriel Marques Tavares

  5. Department of Engineering and Architecture, University of Trieste, Trieste, Italy

    Sylvio Barbon Junior

Authors
  1. Leonardo Arrighi
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Correspondence to Leonardo Arrighi or Luca Pennella .

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Editors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

  2. Fraunhofer Institute for Telecommunications, Berlin, Germany

    Sebastian Lapuschkin

  3. University of Marburg, Marburg, Germany

    Christin Seifert

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Arrighi, L., Pennella, L., Marques Tavares, G., Barbon Junior, S. (2024). Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles. In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2154. Springer, Cham. https://doi.org/10.1007/978-3-031-63797-1_16

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  • DOI: https://doi.org/10.1007/978-3-031-63797-1_16

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