Prediction Interval Calculation for AI Model Uncertainty
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Solution Overview
Problem
Existing AI models face challenges in calculating the standard error of prediction, especially for regression models where predicted values are not available, making it difficult to gauge performance without access to training data or feedback.
Innovation Solution
A computer-implemented method for determining a prediction interval function based on datasets, calculating prediction interval parameters through regression models, creating transformed predictions using logical distance, and selecting a transformed prediction model based on mean absolute correlation to output a selected model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction interval methods are used for regression models, then model performance can be evaluated, but training data and feedback are required which are often not available
Solution Approach 1:
The patent creates synthetic copies of training data through data synthesis techniques, generating artificial training datasets that replicate the statistical properties and patterns of the original training data. This allows the model to be retrained or evaluated without accessing the actual proprietary training data, thus preserving measurement precision while avoiding information loss.
Solution Approach 2:
The patent introduces an intermediary mechanism that acts as a bridge between the unavailable training data and the evaluation process. This intermediary synthesizes or approximates the necessary training data characteristics, enabling performance measurement without direct access to the original training dataset.
2Measurement precision
If multiple regression models are trained to calculate prediction intervals, then more accurate performance metrics can be obtained, but computational complexity and time increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing key statistical properties, prediction intervals, and performance metrics during an initial training phase. These pre-computed values are then reused for subsequent evaluations, eliminating the need to retrain multiple models repeatedly and significantly reducing computation time while maintaining metric accuracy.
Solution Approach 2:
The patent applies partial action by training and evaluating only the necessary subset of regression models required for accurate performance measurement, rather than exhaustively training all possible model combinations. This selective approach achieves sufficient measurement precision without the excessive time cost of comprehensive model evaluation.
3Productivity
If AI models are deployed without standard error calculation, then deployment speed increases, but reliability and performance monitoring decrease
Solution Approach 1:
The patent enables the AI model to perform self-service by automatically calculating its own standard error and performance metrics during deployment. The model incorporates built-in mechanisms to generate prediction intervals and evaluate its own performance without requiring external computational resources or complex monitoring infrastructure, thus maintaining deployment speed while ensuring reliability.
Data Source
AI summary
An approach for selecting a transformed prediction model for estimating the uncertainty of prediction associated with machine learning is disclosed. The approach determines a function corresponding to a prediction interval based on one or more datasets and calculates one or more sets of prediction interval parameters associated with the function based on training a set of regression models with the one or more datasets. The approach creates one or more transformed predictions based on the one or more sets of parameters and based on a logical distance and selects a transformed prediction model based on a mean absolute correlation from the one or more transformed predictions. Furthermore, the approach outputs the selected transformed prediction model.