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TREE-BASED MACHINE LEARNING

Learning with explainable trees

Nature Machine Intelligence volume 2, pages 16–17 (2020)Cite this article

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Tree-based models are among the most popular and successful machine learning algorithms in practice. New tools allow us to explain the predictions and gain insight into the global behaviour of these models.

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Fig. 1: Explanation of tree-based models.

References

  1. The State of Data Science and Machine Learning 2017 (Kaggle, 2017); https://www.kaggle.com/surveys/2017

  2. Chen, T. & Guestrin, C. in Proc. of the 22nd ACM SIGKDD 785–794 (ACM, 2016).

  3. Samek, W. et al. (eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (Springer, 2019)

  4. Kauffmann, J. et al. Preprint at https://arxiv.org/abs/1906.07633 (2019).

  5. Lundberg, S. et al. Nat. Mach. Intell. https://doi.org/10.1038/s42256-019-0138-9 (2020).

  6. Shapley, L. in Contributions to the Theory of Games Vol. 2 (eds Kuhn, H. W. & Tucker, A. W.) 307–317 (Princeton Univ. Press, 1953).

  7. Lapuschkin, S. et al. Nat. Commun. 10, 1096 (2019).

    Article  Google Scholar 

  8. Bryce, G. & Flaxman, S. AI Mag. 38, 350–357 (2017).

    Google Scholar 

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  1. Machine Learning Group, Fraunhofer Heinrich Hertz Institute, Berlin, Germany

    Wojciech Samek

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  1. Wojciech Samek
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Correspondence to Wojciech Samek.

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Samek, W. Learning with explainable trees. Nat Mach Intell 2, 16–17 (2020). https://doi.org/10.1038/s42256-019-0142-0

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