VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees

arXiv:2112.00334v5 Announce Type: replace
Abstract: Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and …

abstract analytics arxiv boosting cs.hc cs.lg decision decision trees ensemble however machine machine learning ml models performance popular predictive rules stat.ml trees type visual visual analytics