Machine Learning Model Analysis via Emulation
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Solution Overview
Problem
Sophisticated machine learning models are difficult to analyze due to their complexity, making it challenging for data analysts to determine the reasons behind specific model decisions, especially in applications like credit approval, where changes in inputs lead to unexpected changes in outcomes.
Innovation Solution
Automated tools generate simplified machine learning models that emulate the behavior of more complex models, allowing analysis of which input changes cause decision changes by creating emulation models and using techniques like Tree SHAP to identify impactful features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sophisticated machine learning models are used to address larger and more complex problems, then problem-solving capability is improved, but model complexity increases making the model operate as a black box
Solution Approach 1:
The patent creates simplified emulation models that copy the essential behavior of complex machine learning models. These emulation models replicate the decision-making patterns and feature importance relationships of the original complex models but in a transparent, interpretable format that data analysts can understand and trust.
Solution Approach 2:
The patent segments the complex model into separate components by identifying and isolating individual feature importance relationships. Through techniques like Tree SHAP analysis, the model decomposes its decision logic into discrete, explainable segments that show which features drive specific predictions, making the black box transparent.
2Productivity
If complex machine learning models are deployed to handle important problems, then productivity is improved, but ease of operation deteriorates as analysts cannot determine why specific results are provided
Solution Approach 1:
The patent introduces emulation models as intermediary representations between the complex machine learning model and the data analyst. These intermediaries translate the opaque decisions of the complex model into clear, actionable explanations that analysts can operate with confidence, bridging the gap between model capability and human understanding.
Solution Approach 2:
The patent implements feedback mechanisms where the system provides explanations for its predictions and allows analysts to query the model decisions. This feedback loop enables analysts to understand why specific results are provided and trust the model's reasoning, improving ease of operation while maintaining high productivity.
3Ease of operation
If traditional analysis techniques are used on complex models, then ease of operation is maintained, but ability to detect and measure input impact deteriorates
Solution Approach 1:
The patent replaces traditional mechanical analysis techniques with machine learning-based emulation models and Tree SHAP algorithms. This substitution enables the system to automatically detect and measure the impact of input changes on model decisions, overcoming the limitations of manual analysis methods while maintaining ease of operation through automated processing.
Data Source
AI summary
Disclosed are various embodiments for analyzing machine learning models. A selection is obtained of a first tuple comprising a first feature vector and a first result generated by a machine learning model and a second tuple comprising a second feature vector and a second result generated by the machine learning model. Then, a plurality of emulated feature vectors are generated. Next, a plurality of emulated results are generated. Subsequently, a plurality of emulated decision instances are generated. Next, a decision tree is built based at least in part on the first tuple, the second tuple, and the plurality of emulated decision instances. Finally, an importance of each feature on the decision tree is computed.