Machine Learning Uncertainty Quantification With Stepdown Model Switching
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Solution Overview
Problem
Machine learning models often produce unreliable results due to a lack of understanding of their underlying behavior, leading to errors and biases, and there is a need for quantifying model uncertainty to ensure responsible decision-making.
Innovation Solution
A method is provided to determine uncertainty values and confidence intervals for machine learning model outputs, switching to a more robust stepdown model when uncertainty exceeds a threshold, and iteratively training the model to minimize predictive variance by removing high-variance feature interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning model is used to make predictions, then productivity is improved, but reliability deteriorates due to uncertainty and lack of understanding of model behavior
Solution Approach 1:
The patent introduces an uncertainty quantification module as an intermediary between the machine learning model and the decision-making process. This module calculates confidence intervals and uncertainty values for model predictions, providing a mediator layer that assesses prediction reliability before final decisions are made, thus resolving the contradiction between maintaining productivity and ensuring reliability
Solution Approach 2:
The system implements feedback by using uncertainty measurements to adjust model behavior. When uncertainty exceeds thresholds, the system switches to alternative models or requests additional data, creating a feedback loop that continuously monitors and adjusts prediction reliability based on measured uncertainty, thereby maintaining both productivity and reliability
2Manufacturing precision
If model complexity is increased to improve predictive accuracy, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent applies dynamics by making model complexity adaptive rather than static. The system dynamically selects between different model complexities based on uncertainty thresholds and input characteristics, using simple models when sufficient and complex models only when necessary, thus achieving high predictive accuracy without permanently increasing device complexity
Solution Approach 2:
The system segments the modeling task into multiple models of varying complexity. Instead of using one highly complex model for all cases, the patent divides the problem into multiple specialized models with different complexity levels, selecting the appropriate segment based on the specific prediction task, thereby reducing overall device complexity while maintaining manufacturing precision
3Reliability
If ensemble methods are used to reduce uncertainty, then reliability is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent applies partial action by using ensemble methods selectively rather than universally. Instead of deploying full ensemble methods for all predictions, the system uses uncertainty quantification to identify only those cases where ensemble methods are necessary, applying them partially to specific high-uncertainty predictions while using simpler methods for confident predictions, thus improving reliability without excessive complexity
Data Source
AI summary
Computer-implemented machines, systems and methods for providing insights about uncertainty of a machine learning model. A method includes determining an uncertainty value associated with a first machine learning model output of a first machine learning model. The method further includes generating a confidence interval for the first machine learning model output associated with an input. The method further includes switching, responsive to the uncertainty value satisfying a threshold, from the first machine learning model to a second machine learning model, the second machine learning model generating a second machine learning model output. The method further includes generating the second machine learning model. The method further includes providing, responsive to the switching, the machine learning output, the uncertainty value, the confidence interval, and the second machine learning output to a user interface.