Example: Interactive Exploration of Trained Ensemble Classifier

This visual analytics workflow enables domain experts without machine learning expertise to extract interpretable decision rules from complex trained classifiers. The approach addresses a common challenge: making black-box ensemble models (like Random Forests) accessible to practitioners who need actionable insights rather than raw model predictions. The workflow consists of three main phases:

1. Phase 1: Automated Model Structuring - The system automatically analyzes the ensemble's internal structure by computing component similarities, grouping similar decision components, and creating spatial overviews. This preprocessing transforms an opaque ensemble into an organized landscape ready for exploration.
2. Phase 2: Progressive Component Selection - Through multiple coordinated visualizations, users progressively narrow from the entire model down to individual promising components. This involves iterative cycles of visualization, quality assessment, and selection decisions at multiple granularity levels (groups → representatives → individuals).
3. Phase 3: Rule Derivation - Once a suitable component is identified, users explore its detailed structure through interactive visualization, assess interpretability, optionally refine decision boundaries using domain knowledge, and extract human-readable rules for operational deployment.