Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification.

ABSTRACT: Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability is germane. In metabolomics, partial least square discriminant analysis (PLS-DA) and its variants are widely used, partly due to the model's interpretability with the Variable Influence in Projection (VIP) scores, a global interpretable method. Herein, Tree-based Shapley Additive explanations (SHAP), an interpretable ML method grounded in game theory, was used to explain ML models with local explanation properties. In this study, ML experiments (binary classification) were co

SUBMITTER: Bifarin OO 

PROVIDER: S-EPMC10159207 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature