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Protocol to calculate and compare exact Shapley values for different kernels in support vector machine models using

Jannik P Roth1, Jürgen Bajorath1

  • 1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany; Lamarr Institute for Machine Learning and Artificial Intelligence, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany.

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Summary
This summary is machine-generated.

This study introduces a protocol for calculating exact Shapley values in machine learning models, specifically support vector machines. This method enhances the explainability of AI predictions using game theory concepts.

Keywords:
bioinformaticschemistrycomputer sciences

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Area of Science:

  • Computational chemistry
  • Machine learning
  • Artificial intelligence

Background:

  • Machine learning models, particularly support vector machines (SVMs), are increasingly used in scientific research.
  • Explaining the predictions of these complex models is crucial for trust and validation.
  • The Shapley value formalism offers a theoretically sound method for attributing contributions to model predictions.

Purpose of the Study:

  • To present a detailed protocol for calculating exact Shapley values for SVM models.
  • To enable comparison of Shapley values across different kernels and binary input features.
  • To provide a framework for analyzing and interpreting feature importance in SVM predictions.

Main Methods:

  • Adaptation of the Shapley value formalism from cooperative game theory.
  • Development of a protocol for calculating exact Shapley values for SVMs.
  • Utilization of customizable Python scripts for data preparation and value computation.
  • Implementation of correlation analysis and feature mapping for results interpretation.

Main Results:

  • A reproducible protocol for computing exact Shapley values for SVM models was established.
  • The protocol facilitates the comparison of feature importance across various SVM configurations.
  • Methods for analyzing and visualizing Shapley value results were detailed.

Conclusions:

  • The presented protocol provides a robust method for enhancing the interpretability of SVM models.
  • This approach allows for a deeper understanding of feature contributions to model predictions.
  • The Shapley value formalism, when applied to SVMs, offers valuable insights into AI model behavior.