Machine Learning Model Interpretation via Rule Extraction
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Solution Overview
Problem
Complex machine learning models, such as neural networks and gradient boosted trees, are difficult to interpret, leading to a trade-off between model accuracy and interpretability, as their inner workings are invisible and not readily understandable, limiting the ability to validate their behavior and understand the data they were trained on.
Innovation Solution
A method is provided to generate a set of rules that describe the behavior of a machine learning model at a global level, using instance level conditions and a genetic algorithm to create class level rules that explain how the model classifies inputs, allowing for improved model interpretation and validation without compromising accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models (neural networks, gradient boosted trees) are used to improve model accuracy, then prediction performance is improved, but interpretability deteriorates because the models become black boxes that are difficult to validate and understand
Solution Approach 1:
The patent introduces an intermediary system that translates complex model predictions into simple, human-understandable rules. This intermediary layer processes the black box model's output and generates interpretable if-then rules that explain the model's decision-making logic, allowing users to understand and validate complex model behavior without sacrificing accuracy.
Solution Approach 2:
The patent extracts the essential decision-making logic from complex machine learning models by identifying and isolating the key rules that drive predictions. This extraction process separates the interpretable rule-based component from the complex model, enabling validation and understanding of model behavior while maintaining the high accuracy of the original complex model.
2Measurement precision
If complex machine learning models are used to improve prediction accuracy, then model performance is improved, but the ability to validate model behavior deteriorates because every possible input sequence cannot be analyzed
Solution Approach 1:
The rule extraction system acts as a mediator that bridges the gap between complex model predictions and human validation capabilities. By translating model behavior into simple, verifiable rules, it enables thorough validation of model logic without requiring exhaustive testing of all possible input sequences, thus maintaining both accuracy and validation confidence.
Solution Approach 2:
The patent transforms the validation approach by changing the parameter of validation from exhaustive input sequence analysis to rule-based logic verification. This parameter change enables comprehensive validation of model behavior by focusing on the underlying decision rules rather than attempting to test every possible input combination.
3Ease of operation
If simpler machine learning models (linear regression, decision trees) are used to improve interpretability, then model understanding is improved, but model accuracy deteriorates
Solution Approach 1:
The patent merges the advantages of both simple and complex models by combining the high prediction accuracy of complex models with the interpretability of simple rule-based systems. The system uses complex models for accurate predictions while simultaneously extracting simple, interpretable rules that explain the predictions, thus achieving both accuracy and interpretability simultaneously.
Solution Approach 2:
The patent segments the modeling process into two distinct components: a complex model for accurate prediction and a rule extraction system for interpretability. This segmentation allows each component to excel at its specific function while working together to provide both high accuracy and easy interpretation.
Data Source
AI summary
A technique is disclosed for generating class level rules that globally explain the behavior of a machine learning model, such as a model that has been used to solve a classification problem. Each class level rule represents a logical conditional statement that, when the statement holds true for one or more instances of a particular class, predicts that the respective instances are members of the particular class. Collectively, these rules represent the pattern followed by the machine learning model. The techniques are model agnostic, and explain model behavior in a relatively easy to understand manner by outputting a set of logical rules that can be readily parsed. Although the techniques can be applied to any number of applications, in some embodiments, the techniques are suitable for interpreting models that perform the task of classification. Other machine learning model applications can equally benefit.