Interactive Visualization of Ensemble Decision Trees based on the Relations among Weak Learners

Ensemble learning that combines multiple weak learners for enhanced performance, is widely used but suffers from low interpretability/explainability. This leads challenges not only in operational aspects like model maintenance and quality assurance but also in addressing societal needs such as fairness and privacy. To tackle this, we propose a new visualization method focusing on the relationship among weak learners in ensemble models to improve understanding of the model structure and its learning processes. In this paper, we defined the relation between weak learners based on a "common sample" in gradient-boosting decision trees, and a visualization method as a three-dimensional graph structure was proposed. Ensemble models trained with synthetic data sets that include typical distribution shifts and real-world open data sets were visualized. As a result, we demonstrated that this approach enables a more accessible understanding of the behavior and structure of ensemble models comprising multiple weak learners, facilitating the identification of overfitting and underfitting through visualization of changes during the training and validation processes.