Visual Explanation of Machine Learning Models in Shifted Paired Coordinates in 3D

Abstract—Machine learning (ML) methods achieved remarkable success recently. However, the trust by domain experts for many new black-box models is quite low. Visualization is a natural way to involve domain experts to the process of development of explainable models, which can mitigate the deficiencies of black-box models. It is typically easier for humans to understand and reason within visualizations of data and models. Recent Sequential Rule Generation (SRG) algorithms for categorical qualitative data, and a lossless visualization system in 3-D based on the Shifted Paired Coordinates (SPC-3D) allow producing ML models as (1) interpretable approximators of black-boxes or as (2) independent interpretable models with accuracy comparable with black boxes on the same data. However, SRG can generate a large set of rules that are difficult to analyze and visualize. This paper proposes a new algorithm to Join and Modify Rules (JMR), which creates a smaller set of rules with the same precision and coverage for a given set of rules. It is explored in the case studies, which show its efficiency along with SPC-3D visualization system for getting trustable models.

Recommended citation: Kovalerchuk, Boris & Martinez, Joshua & Fleagle, Michael. (2024). Visual Explanation of Machine Learning Models in Shifted Paired Coordinates in 3D. 1-8. 10.1109/IV64223.2024.00052.
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