SHAP-based binarization enhances metataxonomic machine learning with application to gut microbiota of inflammatory bowel disease.

ABSTRACT: Machine learning has been increasingly applied to microbiome data for biomarker discovery. However, microbiome datasets are typically high-dimensional, sparse, and correlated, which makes model training challenging and prone to overfitting. Previous studies have also reported that microbiome features exhibit binary-like characteristics, and that binarization does not necessarily reduce predictive performance. This observation motivated our work. Building on this idea, we propose a SHAP-based binarization pipeline. We first trained several machine learning models on raw continuous data and selected the best-performing model (random forest). Using SHAP values derived from the training set, we determined feature-specific thresholds that best separated positive and negative contributions. The

SUBMITTER: Lee Y 

PROVIDER: S-EPMC12594890 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature