Machine Learning Model Weighting via Feature Sparsity Metrics
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Solution Overview
Problem
Machine learning models often rely on numerous features for predictions, leading to inefficiencies and complexity, as they do not effectively identify and prioritize features contributing to specific predictions, resulting in resource wastage and less accurate outcomes.
Innovation Solution
A system that generates and updates machine learning models by assigning weights based on the impact of features, using sparsity metrics to determine the number of features contributing to each prediction, thereby simplifying the models and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models use a large number of features to generate predictions, then prediction accuracy is improved, but model complexity and resource consumption increase
Solution Approach 1:
The patent segments the feature set by computing sparsity metrics for individual features and grouping them based on their contribution to predictions. Features are divided into significant and insignificant groups, allowing the model to process only relevant features for each prediction, thereby reducing complexity while maintaining accuracy.
Solution Approach 2:
The patent applies local quality by assigning different weights to different features based on their sparsity metrics. Each feature is evaluated individually to determine its local importance to specific predictions, allowing the model to focus computational resources on high-impact features rather than treating all features uniformly.
2Measurement precision
If machine learning models use a large number of features to generate predictions, then prediction accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent extracts and removes insignificant features from the prediction process by identifying features with low sparsity metrics. By taking out these non-contributing features, the model reduces computational workload and resource consumption while retaining only the essential features needed for accurate predictions.
Solution Approach 2:
The patent applies partial action by processing only a subset of features (those with high sparsity metrics) rather than all available features. This selective processing reduces resource consumption while maintaining prediction accuracy, as the model performs computations only on the necessary portion of the feature set.
3Productivity
If machine learning models are simplified by reducing coefficients or branches, then resource efficiency is improved, but the ability to capture specific feature contributions to individual predictions is lost
Solution Approach 1:
The patent performs preliminary action by computing sparsity metrics for all features before the actual prediction process. This pre-computation identifies which features will be important for each prediction, allowing the model to be simplified in advance while preserving the ability to capture specific feature contributions when needed, thus maintaining both efficiency and information accuracy.
Data Source
AI summary
Methods and systems are described herein for updating machine learning models using weights. The system inputs, into a machine learning model, a dataset including entries and features to obtain a relative impact of each feature. The system generates, using the relative impacts, a sparsity metric for each entry, each sparsity metric indicating a measure of a number of features used to generate a corresponding prediction. The system retrieves a sparsity threshold for assigning weights to the plurality of entries. The system generates an updated dataset based on assigning, to each entry within the dataset, a corresponding weight. Each corresponding weight is determined based on a relation of the sparsity metric to the sparsity threshold. The system inputs, into the machine learning model, the updated dataset to update the machine learning model based on the corresponding weights, where the machine learning model relies more heavily on entries with higher corresponding weights.