Feature Contribution Score Labeling for Consistent ML Interpretation
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Solution Overview
Problem
Non-expert users face challenges in interpreting machine learning feature contribution scores due to their numeric nature and variability across different models, leading to inconsistent and difficult interpretation.
Innovation Solution
A feature contribution score category classification framework that automatically maps feature contribution scores to interpretable category labels, utilizing an algorithm optimization component to ensure consistent and accurate labeling across varying feature set sizes, independent of the underlying machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature contribution scores are provided in numeric format, then measurement precision is improved, but ease of operation deteriorates due to difficulty in interpretation for non-experts
Solution Approach 1:
The patent applies color-coded labels (e.g., red for high impact, yellow for medium impact, green for low impact) to feature contribution scores. This visual encoding transforms abstract numeric values into intuitive color categories that non-expert users can immediately understand, resolving the contradiction between precise measurement and ease of interpretation.
Solution Approach 2:
The system automatically transforms the parameter representation from raw numeric scores to categorized labels with associated visual indicators. This parameter transformation maintains the underlying precision while presenting the data in a more accessible format through automatic classification and visual encoding.
2Reliability
If feature contribution scores are standardized across models, then reliability is improved, but adaptability deteriorates due to variability in different model types
Solution Approach 1:
The patent implements a universal labeling framework that works across multiple machine learning model types (e.g., random forest, gradient boosting, neural networks). The system automatically adapts to different model architectures while applying consistent interpretation rules, enabling reliable cross-model comparison without sacrificing adaptability to specific model characteristics.
Solution Approach 2:
The system dynamically adjusts its analysis approach based on the specific model type being evaluated, while maintaining consistent output labeling. This dynamic adaptation allows the framework to handle variability in different model architectures while producing standardized, reliable results across all model types.
3Measurement precision
If detailed feature contribution analysis is provided, then measurement precision is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent extracts and separates the complex computation logic from the user interface layer. The system performs detailed feature contribution analysis through automated algorithms while presenting only the essential categorized results to users. This extraction of computational complexity maintains measurement precision while simplifying the user-facing system complexity.
Solution Approach 2:
The system implements self-service automation where the framework automatically performs feature contribution analysis, categorization, and visualization without requiring manual configuration or complex user setup. This automated self-service approach maintains high measurement precision while reducing the effective system complexity from the user perspective.
Data Source
AI summary
A system may obtain a plurality of historical feature contribution score (FCS) datasets, each historical FCS dataset comprising a first plurality of feature contribution scores and a size of the historical FCS dataset. The system may apply default feature contribution category classification (FCCC) parameters to the plurality of historical FCS datasets and may optimize the default FCCC parameters to produce a plurality of optimized FCCC parameters. The system may produce a training dataset comprising the optimized FCCC parameters and use the training dataset to train a machine learning model to apply the category classification labels. The system may apply the new FCS dataset to the machine learning model, the new FCS dataset comprising a second plurality of feature contribution scores and a size of the new FCS dataset, and provide the category classification labels for the new FCS dataset to a user interface.