Interpretable Rule Set Difference Metrics for ML Model Changes
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Solution Overview
Problem
Existing machine learning models lack interpretability, making it difficult for users to understand model changes and trust the categorization results, especially when comparing different models or versions.
Innovation Solution
A computer-implemented method generates a difference metric and mapping between interpretable rule sets of different machine learning models, allowing for the comparison and contrast of classifications using interpretable conjunctions and rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used for categorization, then accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent introduces an intermediary layer between the machine learning model and the user by generating interpretable rules that translate model predictions into human-understandable logic. These rules act as a mediator that preserves the accuracy of the underlying model while providing explainable reasoning for each classification decision.
Solution Approach 2:
The patent creates a simplified copy or representation of the complex model's decision logic in the form of interpretable rules. This rule-based representation captures the essential classification logic without requiring users to understand the complex underlying model structure, thus maintaining accuracy while improving interpretability.
2Productivity
If summary metrics are used to evaluate models, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the model evaluation process into two complementary parts: summary metrics for quick productivity assessment and interpretable rules for detailed change detection. The rules are generated separately from the model predictions, allowing users to focus on specific aspects of model behavior that matter most to their domain.
Solution Approach 2:
The patent adds a new dimension to model evaluation by transforming predictions into rule-based explanations. This dimensional transformation enables detection of qualitative changes in model behavior that summary metrics alone cannot capture, such as shifts in decision boundaries or feature importance patterns.
3Reliability
If concept drift detection methods are applied, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent enables the system to self-explain its own behavior by automatically generating interpretable rules from model predictions. This self-service capability allows the model to provide its own reasoning without requiring complex external analysis tools or manual inspection of model internals.
Solution Approach 2:
The patent transforms the representation of model outputs from raw predictions to rule-based parameterizations. By expressing model behavior in terms of if-then rules with clear conditions and consequences, the system achieves reliable change detection while maintaining simplicity in how the changes are presented and understood.
Data Source
AI summary
In a method for interpreting output of a machine learning model, a processor receives a first interpretable rule set. A processor may also receive a second interpretable rule set generated from a dataset and model-predicted labels classifying the dataset. A processor may also generate a difference metric and mapping between the first interpretable rule set and the second interpretable rule set.