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Towards Explainability of Tree-Based Ensemble Models. A Critical Overview

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New Advances in Dependability of Networks and Systems (DepCoS-RELCOMEX 2022)

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Abstract

Tree-based ensemble models are widely applied in artificial intelligence systems due to their robustness and generality. However, those models are not transparent. For the sake of making systems trustworthy and dependable, multiple explanation techniques are developed.

This paper presents selected explainability techniques for tree-based ensemble models. First, the aspect of black-boxness and the definition of explainability are reported. Then, predominant model-agnostic (LIME, SHAP, counterfactual explanations), as well as model-specific techniques (fusion into a single decision tree, iForest) are described. Moreover, other methods are also briefly mentioned. Finally, a brief summary is presented.

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Authors and Affiliations

  1. AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Krakow, Poland

    Dominik Sepiolo & Antoni Ligęza

Authors
  1. Dominik Sepiolo
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  2. Antoni Ligęza
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Corresponding author

Correspondence to Dominik Sepiolo .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Wojciech Zamojski

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Jacek Mazurkiewicz

  3. Wrocław University of Science and Technology, Wrocław, Poland

    Jarosław Sugier

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Tomasz Walkowiak

  5. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Sepiolo, D., Ligęza, A. (2022). Towards Explainability of Tree-Based Ensemble Models. A Critical Overview. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) New Advances in Dependability of Networks and Systems. DepCoS-RELCOMEX 2022. Lecture Notes in Networks and Systems, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-06746-4_28

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