VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees
This addresses the need for trustworthy and explainable decisions in domains like finance and healthcare, though it is incremental as it builds on existing ensemble methods with a new visualization approach.
The paper tackles the problem of interpreting complex ensemble decision trees (e.g., from bagging and boosting) by developing VisRuler, a visual analytics tool that helps users extract decision rules through a visual workflow, and evaluation showed most users successfully used it to explore rules and answer questions.
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 of bagging and boosting algorithms, such as random forest and adaptive boosting, reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.