Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping

arXiv:2404.17886v1 Announce Type: new
Abstract: Interpretable machine learning has emerged as central in leveraging artificial intelligence within high-stakes domains such as healthcare, where understanding the rationale behind model predictions is as critical as achieving high predictive accuracy. In this context, feature selection assumes a pivotal role in enhancing model interpretability by identifying the most important input features in black-box models. While random forests are frequently used in biomedicine for their remarkable performance on tabular datasets, the accuracy gained from aggregating …

abstract accuracy application artificial artificial intelligence arxiv context cs.ai cs.lg disease domains feature feature selection graphs healthcare intelligence machine machine learning pivotal predictions predictive role tree type understanding unsupervised