A Visual Analytics Conceptual Framework for Explorable and Steerable Partial Dependence Analysis.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Machine learning models are crucial across diverse fields, but their interpretability is a growing concern.
• Partial Dependence Plots (PDP) are model-agnostic tools for understanding feature influence, yet face limitations in visual interpretation, accuracy, and computational complexity, especially with multiple features.

Purpose of the Study:

• To propose a conceptual framework that enhances the analysis of machine learning model interpretability by addressing the limitations of standard Partial Dependence Plots.
• To enable more effective and efficient exploration of feature effects in machine learning models.

Main Methods:

• Development of a conceptual framework for interactive and incremental analysis of partial dependences.
• Incorporation of user-guided computation on selected subspaces to manage combinatorial complexity.
• Validation through expert knowledge and development of a prototype (W4SP).

Main Results:

• The proposed framework mitigates limitations of standard PDPs, including visual interpretation, aggregation issues, and computational costs.
• Users can explore and refine partial dependences, obtain incrementally accurate results, and focus computations on specific subspaces.
• Significant savings in computational and cognitive resources compared to traditional batch processing of all feature combinations.

Conclusions:

• The novel framework offers a more efficient and insightful approach to machine learning model interpretability.
• It empowers users to better understand complex feature interactions by overcoming the computational and cognitive challenges of standard PDPs.
• The W4SP prototype demonstrates the practical applicability and advantages of the proposed framework in real-world case studies.