Lightweight ML Models via Explainability Vector Feature Recombination
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Solution Overview
Problem
Conventional systems fail to effectively leverage explainability vectors for feature selection and recombination in machine learning models, leading to challenges in adapting AI models for improved prediction accuracy in resource consumption analysis.
Innovation Solution
The method involves extracting explainability vectors from trained machine learning models to identify correlated features, performing principal component analysis or factor analysis, and generating a new set of features using these vectors to improve the predictive power of subsequent models, with an encoding map translating values between feature sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning models use all available features for prediction, then prediction coverage is comprehensive, but model complexity increases and prediction accuracy may be reduced due to noise from less relevant features
Solution Approach 1:
The patent extracts an explainability vector from the trained machine learning model to identify and separate relevant features from irrelevant ones. This extraction process isolates the most important features that contribute to prediction accuracy, removing noise from less relevant features while maintaining model effectiveness.
Solution Approach 2:
The patent transforms the original feature set by applying the explainability vector to generate a new set of features with optimized characteristics. This parameter transformation reweights and recombines features based on their actual contribution to predictions, converting a complex full-feature model into a streamlined version with improved accuracy.
2Productivity
If machine learning models process complete feature sets, then information completeness is maintained, but processing time and computational resources increase
Solution Approach 1:
The explainability vector extraction process identifies and extracts only the most informative features, separating them from redundant data. This selective extraction maintains the essential information needed for accurate predictions while eliminating unnecessary computational overhead from processing complete feature sets.
Solution Approach 2:
The patent segments the feature set into relevant and irrelevant components using the explainability vector. By dividing the complete feature set into distinct segments based on their contribution to predictions, the system processes only the essential segment, improving efficiency without losing critical information.
3Measurement precision
If machine learning models use explainability vectors for feature selection, then prediction accuracy improves, but the process complexity for feature extraction and transformation increases
Solution Approach 1:
The machine learning model generates its own explainability vector internally, performing self-analysis of feature importance. This self-service approach eliminates the need for external complex feature selection processes, as the model autonomously identifies and extracts relevant features through its own operational data, simplifying the overall feature processing pipeline.
Data Source
AI summary
Systems and methods for generating lightweight or surrogate models using explainability vectors. In some aspects, the system receives a first machine learning model trained to determine resource consumption by a user system. The first machine learning takes as input a first set of features. The system processes the first machine learning model to extract an explainability vector. Based on the explainability vector, the system rearranges the first set of features to generate a second set of features. The system processes the values for the first set of features to generate values for the second set of features corresponding to user profiles and trains a second machine learning model which takes as input the second set of features.