VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees. (arXiv:2112.00334v2 [cs.LG] UPDATED)

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 health care. Thus, the
interpretability …

analytics arxiv decision rules trees visual analytics