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

TL;DR

VisRuler is a visual analytics tool designed to help users interpret and extract decision rules from complex ensemble machine learning models like random forests and boosting methods, enhancing explainability.

Contribution

The paper introduces a novel visual analytics workflow for extracting and understanding decision rules from ensemble models, improving interpretability in critical domains.

Findings

Most users successfully explored decision rules visually.

The system facilitated understanding of model decisions.

Users found the tool effective for global and local explanations.

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 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.