Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles.

Area of Science:

• Machine Learning
• Data Visualization
• Artificial Intelligence

Background:

• Classification models are crucial in machine learning, but traditional metrics often lack interpretability.
• Interpreting model decisions is increasingly important, shifting focus beyond quantitative metrics.
• Visualization techniques aid interpretability, yet struggle with large, complex models like Random Forests.

Purpose of the Study:

• To introduce a novel visualization method, Explainable Matrix (ExMatrix), for enhancing Random Forest (RF) model interpretability.
• To address the challenge of visualizing large and complex RF models with numerous rules.
• To facilitate the analysis of entire models and auditing of classification results.

Main Methods:

• Proposing Explainable Matrix (ExMatrix), a novel visualization technique.
• Utilizing a matrix-like visual metaphor where rows represent rules, columns represent features, and cells represent rule predicates.
• Developing a method capable of handling Random Forest models with a massive quantity of rules.

Main Results:

• ExMatrix provides a scalable visualization for complex Random Forest models.
• The method enables the analysis of entire models and auditing of classification outcomes.
• Demonstrated applicability of ExMatrix through practical examples, promoting RF model interpretability.

Conclusions:

• ExMatrix offers a powerful new approach to visualizing and interpreting Random Forest models.
• The technique overcomes limitations of existing methods in handling large-scale rule-based models.
• ExMatrix can be practically applied to improve the transparency and understanding of Random Forest classifications.