Rule List Extraction From Tree Ensembles With Cross-Tree Fidelity
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Solution Overview
Problem
Tree ensemble models lack transparency and interpretability, leading to difficulties in understanding their decision logic, especially for critical tasks, and existing rule extraction methods fail to consider cross-tree interactions, resulting in lower fidelity.
Innovation Solution
Generate rule lists that consider cross-tree interactions, providing an exact or tunable precision decomposition of the decision-making process, allowing for human-understandable and simplified rule lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tree ensemble models are used to improve predictive performance, then prediction accuracy is improved, but transparency and interpretability deteriorate
Solution Approach 1:
The patent creates a simplified copy of the tree ensemble model in the form of a rule list that mimics the decision-making process. The rule list captures the essential logic of the complex model in an interpretable format, allowing users to understand the decision rationale without sacrificing prediction accuracy significantly
Solution Approach 2:
The patent introduces rule lists as an intermediary representation between the complex tree ensemble model and human users. This intermediary translates the internal logic of the model into human-understandable if-then rules, bridging the gap between high accuracy and interpretability
2Difficulty of detecting and measuring
If conventional rule extraction methods are used to improve interpretability, then transparency is improved, but fidelity deteriorates due to ignoring cross-tree interactions
Solution Approach 1:
The patent merges rules from multiple individual trees into a unified rule list that captures cross-tree interactions. By combining the decision logic across all trees in the ensemble, the method preserves the collective intelligence of the model while maintaining interpretability through the rule list format
Solution Approach 2:
The patent transitions from analyzing individual trees in isolation to considering the multi-dimensional interaction space across all trees. The rule list captures interactions that span multiple trees, adding a new dimension of analysis that conventional single-tree methods cannot achieve
Data Source
AI summary
Systems and methods for extracting rule lists from tree ensembles are provided. A system extracts first stage candidate rules from individual trees. The system identifies the first stage candidate rules that satisfy a precision threshold and places those rules in a solution set. Subsequently, a determination is made whether a further stage is needed based on whether a predetermined number of positive data samples of the data set are covered by the solution set. In the further stage, the system generates next stage candidate rules from previous stage candidate rules that have not been pruned and identifies the next stage candidate rules that satisfy the precision threshold, placing those rules in the solution set. A simplified rule list is generated by identifying a minimum subset of rules in the solution set that covers the positive data samples within the precision threshold.