Explainability of High Energy Physics events classification using SHAP. Issue 1 (1st February 2023)

• Record Type: Journal Article
• Authors: Pezoa, R; Salinas, L; Torres, C
• Is Part Of: Journal of physics. Volume 2438:Issue 1(2023)
• Publication Date: 2023-02-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2438/1/012082
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Complex machine learning models have been fundamental for achieving accurate results regarding events classification in High Energy Physics (HEP). However, these complex models or black-box systems lack transparency and interpretability. In this work, we use the SHapley Additive exPlanations (SHAP) method for explaining the output of two event machine learning classifiers, based on eXtreme Gradient Boost (XGBoost) and deep neural networks (DNN). We compute SHAP values to interpret the results and analyze the importance of individual features, and the experiments show that SHAP method has high potential for understanding complex machine learning model in the context of high energy physics.