Protocol to calculate and compare exact Shapley values for different kernels in support vector machine models using binary features
Year: 2024
Type: article
Abstract: The Shapley value formalism from cooperative game theory was adapted to explain predictions of machine learning models. Here, we present a protocol to calculate and compare exact Shapley values for support vector machine models with commonly used kernels and binary input features. We describe steps for installing software, preparing data, and calculating Shapley values with customizable Python scripts. We then detail procedures for analyzing results via correlation analysis and feature mapping. For complete details on the use and execution of this protocol, please refer to Roth and Bajorath. 1 • Practical guide for calculating exact Shapley values for SVMs with different kernels • Automatic analysis of results for alternative kernels • Visualizations of feature importance for explaining predictions • Step-by-step instructions for using SVEKER in custom research projects Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The Shapley value formalism from cooperative game theory was adapted to explain predictions of machine learning models. Here, we present a protocol to calculate and compare exact Shapley values for support vector machine models with commonly used kernels and binary input features. We describe steps for installing software, preparing data, and calculating Shapley values with customizable Python scripts. We then detail procedures for analyzing results via correlation analysis and feature mapping.
Source: STAR Protocols
Authors: Jannik P. Roth, Jürgen Bajorath
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,500
OpenAlex ID: W4404332042