Interpretive Behavioral Model for ML Feature Influence
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Solution Overview
Problem
Current machine learning models generate accurate outputs but are uninterpretable, failing to provide insights into the factors influencing these outputs, especially as dataset robustness and diversity increase, limiting their interpretability and effectiveness.
Innovation Solution
A method involving a computing device that fits a machine learning trained model to a data feature-set, iteratively removes features, and generates linear models based on accuracy-modifying features to determine their influence on output accuracy, identifying a generative model that exceeds initial accuracy values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are fitted to robust and comprehensive datasets to generate accurate outputs, then output accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent segments the machine learning model into multiple behavioral models, each responsible for specific interpretive functions. This segmentation allows the system to maintain high output accuracy from the comprehensive ML model while separately analyzing and interpreting the contribution of individual features through dedicated behavioral models.
Solution Approach 2:
The patent introduces behavioral models as intermediary components between the input dataset and the machine learning model outputs. These behavioral models serve as mediators that analyze feature contributions and provide interpretive information without interfering with the accuracy of the main ML model's predictions.
2Loss of information
If behavioral models are used to facilitate interpretation of input datasets, then interpretability is improved, but accuracy deteriorates in robust and diverse datasets
Solution Approach 1:
The patent merges multiple behavioral models with the machine learning model to create an integrated system. The behavioral models are combined in a way that their interpretive capabilities enhance the ML model without compromising accuracy, allowing both interpretability and accuracy to coexist in robust and diverse datasets.
Solution Approach 2:
The patent designs behavioral models with multi-functionality, enabling them to serve both interpretive purposes and maintain accuracy across various dataset conditions. These behavioral models are constructed to be universally applicable whether the dataset is limited or robust and diverse, eliminating the trade-off between interpretability and accuracy.
3Loss of information
If multiple behavioral models are created to interpret input datasets, then interpretability is improved, but model complexity increases
Solution Approach 1:
The patent extracts only the essential interpretive components from complex behavioral models, creating simplified versions that retain interpretive functionality while reducing overall model complexity. This extraction approach maintains interpretability without requiring the full complexity of multiple complete behavioral models.
Data Source
AI summary
A method includes fitting a ML trained model to data features, the fitting generates complete data feature-set outputs that are associated with a first set of accuracy values, iteratively fitting, after an iterative removal of each data feature from the data feature-set, the ML trained model to subsets of the plurality of data features to determine respective reduced feature-set outputs, each subset lacks a different data feature of the plurality of data features, determining one or more of the reduced feature-set outputs as corresponding to a second set of accuracy values, designating the iteratively removed data features as accuracy-modifying data features, generating a first linear model, generating a second linear model based on one of the accuracy-modifying data features having a weight that is highest relative to respective different weights of the remaining ones of the accuracy-modifying data features, and identifying the second linear model as a generative model.