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Decision Tree

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Machine Learning Safety

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Abstract

Decision tree is one of the simplest, yet popular, machine learning algorithms. It has a very long history of research and application, and has many variants. This chapter will present a training algorithm for decision trees and discuss several algorithms to identify the safety and security risks of a trained decision tree or its training algorithm.

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Notes

  1. 1.

    A more generic form is f 2 ∈ (b 2 − 𝜖 l, b 2 + 𝜖 u], where both 𝜖 l and 𝜖 u are small numbers that together represents a concise piece of knowledge on feature f 2, i.e., a small range of values around f 2 = b 2. For brevity, we only illustrate the simplified case where 𝜖 l = 𝜖 u = 𝜖.

References

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Author information

Authors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Xiaowei Huang & Gaojie Jin

  2. University of Exeter, Exeter, UK

    Wenjie Ruan

Authors
  1. Xiaowei Huang
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  2. Gaojie Jin
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  3. Wenjie Ruan
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Huang, X., Jin, G., Ruan, W. (2023). Decision Tree. In: Machine Learning Safety. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-19-6814-3_5

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  • DOI: https://doi.org/10.1007/978-981-19-6814-3_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6813-6

  • Online ISBN: 978-981-19-6814-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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