Interpretable Unsupervised Tree Ensembles: Leveraging Feature Graphs for Centrality, Interaction, and Disease Subtyping

The study introduces novel methods to construct feature graphs from unsupervised random forests, capturing feature centrality and discriminating power of feature pairs. These feature graphs are leveraged for effective feature selection and enhanced interpretability, particularly in the context of disease subtyping.