RuleMatrix: Visualizing and Understanding Classifiers with Rules (1807.06228v1)

Abstract: With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. We design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.

Analyzing RuleMatrix: Visualization and Interpretation of Classifiers through Rule-Based Explanations

The paper "RuleMatrix: Visualizing and Understanding Classifiers with Rules" addresses the increasing demand for interpretable machine learning systems. This research proposes a visualization technique that aids non-expert domain users in comprehending the mechanisms underlying predictive models. The focus is on domain experts who rely on machine learning systems without extensive backgrounds in machine learning. RuleMatrix provides an interactive, matrix-based visualization of rule-based representation to interpret model behavior, which can serve to bridge the gap between model developers and end-users by offering insights into the decision-making processes of complex models.

Contributions and Methodology

The authors introduce a systematic approach to convert the complex input-output behavior of machine learning models into comprehensible rule-based representations. The central concept of RuleMatrix is to treat any given model as a "black box" and extract a set of decision rules from its workings. This rule-induction is achieved by treating the original model as an oracle that labels additional data samples. The method involves the following three key steps:

1. Rule Induction: The rule-based approximation of a target black box model is generated using pedagogical model induction, treating the original model as a teacher that labels data for the student model. The algorithm employs model induction techniques such as Scalable Bayesian Rule Lists to yield human-understandable rules.
2. Data Filtering and Explanation: Post extraction, rules are filtered based on user-specified support and confidence thresholds to curate a list that ensures comprehensibility without compromising fidelity. This step addresses common trade-offs between model accuracy and interpretability.
3. Visualization via RuleMatrix: The extracted rules are visualized using a matrix-based layout where rows represent rules and columns represent features. This visual approach allows users to efficiently inspect decision rules, understand the interplay between features, and verify predictions.

Implementation and Evaluation

The effectiveness of RuleMatrix was evaluated through multiple case studies and a usability paper. The presented use cases illustrate RuleMatrix's potential to identify significant decision patterns, offering transparency in machine learning in fields such as healthcare. A user paper confirmed the interface's usability and the participants' capability to successfully comprehend and validate model predictions using rule-based explanations.

Implications and Future Work

The paper provides notable implications for making highly complex machine learning models interpretable to domain experts without requiring them to delve into technical details. Such interpretability is crucial in domains such as healthcare and finance, where decision transparency is paramount.

For future developments, further validation with domain experts in real-world applications could enhance its usability. Additionally, exploring scalability issues to handle larger datasets and more complex models will be critical, alongside examining the practical viability of rule induction techniques across diverse application domains.

The research establishes a solid foundation for future efforts in improving interpretability interfaces in machine learning, suggesting valuable directions for enhancing human-computer interaction in automated decision systems.