Feature Grouping for Interpretable Machine Learning Predictions
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Solution Overview
Problem
Users of machine learning models often lack understanding and confidence in the results due to complexity, especially when applied to specific domains, as they struggle to comprehend how individual features contribute to the outcomes, leading to potential misuse or underutilization of machine learning capabilities.
Innovation Solution
The approach involves grouping features into subsets based on dependencies and contextual contribution values, allowing for the aggregation of contributions to provide higher-level insights, making it easier for users to understand and trust the model outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models use multiple features to improve accuracy, then prediction quality is improved, but user understanding and confidence deteriorate due to complexity
Solution Approach 1:
The patent segments the complex set of features into distinct feature groups based on their relationships and contributions to the prediction. This segmentation allows users to understand the model by examining smaller, more manageable groups of features rather than the entire feature set at once, thereby maintaining prediction accuracy while improving user comprehension.
Solution Approach 2:
The patent introduces feature groups as intermediary concepts between individual features and the final prediction. These feature groups act as mediators that aggregate and organize feature contributions, making the complex relationships more interpretable for users while preserving the full predictive power of the underlying features.
2Measurement precision
If machine learning models process all features to maintain comprehensive analysis, then result accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the feature set into groups that can be processed and analyzed separately. This segmentation enables the system to manage computational complexity by handling feature groups in a structured manner while still considering all features in the overall prediction process, thus maintaining result accuracy without overwhelming computational demands.
Solution Approach 2:
The patent applies partial action by initially focusing analysis on key feature groups that contribute most significantly to the prediction. This allows the system to achieve high accuracy by concentrating computational resources on the most influential features while still having the capability to incorporate additional features if needed, thereby optimizing the balance between accuracy and computational complexity.
3Ease of operation
If machine learning models use contextual contribution values to improve interpretability, then user confidence is improved, but information loss increases due to aggregation
Solution Approach 1:
The patent implements a nested structure where individual features are contained within feature groups, which in turn contribute to the overall prediction. This nesting allows users to drill down from the high-level feature group contributions to individual feature details when needed, preserving complete information while providing simplified views for building user confidence at different levels of granularity.
Solution Approach 2:
The patent makes the level of detail dynamic and adaptable. Users can interact with the model explanation at different levels of granularity, viewing aggregated feature group contributions for high-level understanding or drilling down to individual feature contributions for detailed analysis. This dynamic approach allows the system to provide appropriate levels of information based on user needs, building confidence without permanent information loss.
Data Source
AI summary
Techniques and solutions are described for analyzing results of a machine learning model. A result is obtained for a data set that includes a first plurality of features. A plurality of feature groups are defined. At least one feature group contains a second plurality of features of the first plurality of features. The second plurality of features is less than all of the first plurality of features. Feature groups can be defined based on determining dependencies between features of the first plurality of features, including using contextual contribution values. Group contextual contribution values can be determined for feature groups by aggregating contextual contribution values of the constituent features of the feature groups.