Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles

arXiv:2404.02942v1 Announce Type: new
Abstract: Understanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop interpretation challenge have explored the extraction of the decision structure underlying the model taking advantage of graph simplification and path emphasis. However, while these efforts enhance the visualisation experience, they may either result in a visually complex representation or compromise the interpretability of the original ensemble model. In addressing this challenge, especially in …

abstract arxiv challenge cs.ai cs.lg decision decisions extraction graph graphs however human interpretability interpretation loop machine machine learning machine learning model path pivotal relationships tree type understanding