Variance Characterization Using Shapley Value Feature Attribution
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Solution Overview
Problem
Traditional methods for predicting and evaluating variances between groups are time-consuming and inaccurate, and do not effectively account for synergies between variables, leading to inefficiencies in industries such as finance and game theory.
Innovation Solution
The implementation of Shapley value analysis and machine learning models to automate the determination of feature contributions, using rebalanced data sets and labeled data points to calculate the impact of parameters on changes between data sets, allowing for more accurate predictions and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual hypothesis-based methods are used to determine feature contributions, then flexibility in exploring different scenarios is maintained, but the process becomes time-consuming and prone to accuracy problems
Solution Approach 1:
The patent replaces manual hypothesis-based analysis with an automated machine learning system that uses SHAP (SHapley Additive exPlanations) values to calculate feature contributions. This substitution of manual mechanical analysis with automated computational methods resolves the contradiction by maintaining analytical flexibility while eliminating time consumption and human error.
2Loss of information
If manual hypothesis-based methods are used to determine feature contributions, then exploratory analysis is possible, but accuracy problems and order-dependency arise
Solution Approach 1:
The patent replaces manual hypothesis-based analysis with an automated machine learning system that uses SHAP values to calculate feature contributions. This substitution of manual mechanical analysis with automated computational methods resolves the contradiction by maintaining analytical flexibility while eliminating time consumption and human error.
Solution Approach 2:
The patent transforms the analysis from manual hypothesis testing to automated calculation by changing the fundamental parameter of how feature contributions are determined - using SHAP values derived from machine learning models instead of manual hypothesis evaluation. This parameter change enables accurate, order-independent contribution assessment.
3Reliability
If traditional manual methods are used for variance analysis, then simplicity of process is maintained, but synergies between variables are not accounted for
Solution Approach 1:
The patent replaces manual hypothesis-based analysis with an automated machine learning system that uses SHAP values to calculate feature contributions. This substitution of manual mechanical analysis with automated computational methods resolves the contradiction by maintaining analytical flexibility while eliminating time consumption and human error.
Solution Approach 2:
The patent creates a universal analysis system that can handle multiple variables and their synergies simultaneously through machine learning models. The SHAP value framework provides a unified approach that accounts for interactions between all variables, making the system multi-functional in capturing complex variable relationships.
4Productivity
If automated Shapley value analysis is implemented, then accuracy and efficiency of feature contribution determination are improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing SHAP values for all features across the dataset before conducting the variance analysis. This preliminary calculation stores the computational results in a lookup structure, allowing the final analysis to quickly retrieve and aggregate pre-computed values without repeating expensive computations, thus resolving the contradiction between accuracy and computational complexity.
Data Source
AI summary
Systems, methods, and computer readable media are disclosed for generating, modifying, and using machine learning models to predict and evaluate variances between data sets. Methods disclosed herein may include identifying features that characterize members of a data set, generating a machine learning model using identified features, using the machine learning model and the group to assign feature attributions to the features, and predicting the impact of those features on behaviors of the first data set and a second data set.