Domain-Aware Error Estimate Correction for Machine Learning Models
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Solution Overview
Problem
Training accurate machine learning models faces challenges in selecting input variables, hyperparameters, and determining uncertainty in predictions, as well as identifying whether new observations fall within the robust domain of the trained model.
Innovation Solution
A method involving splitting datasets, training predictive and domain models, computing error and residual values, and using a domain threshold to correct error estimates through curve fitting, thereby improving the accuracy of error estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained to make accurate predictions, then prediction accuracy is improved, but the reliability of error estimates deteriorates
Solution Approach 1:
The patent segments the error estimation process into two distinct components: a predictive model for making predictions and a domain model for assessing prediction reliability. By dividing the single model into specialized components, each can be optimized for its specific function - the predictive model for accuracy and the domain model for error estimate reliability.
Solution Approach 2:
The patent introduces a domain model as an intermediary component that bridges the gap between predictions and reliable error estimates. This domain model acts as a mediator that evaluates whether predictions fall within the model's robust domain and provides corrected error estimates, thereby improving error estimate reliability without compromising prediction accuracy.
2Adaptability or versatility
If the model domain is expanded to handle more diverse observations, then adaptability is improved, but measurement precision of error estimates deteriorates
Solution Approach 1:
The patent implements a dynamic error correction mechanism that adapts based on the input observation. The domain model dynamically assesses whether each prediction falls within the robust domain and applies appropriate error corrections. This allows the system to maintain high error estimate precision for in-domain observations while still handling out-of-domain cases through the domain assessment framework.
Data Source
AI summary
A computing device computes curve descriptive values to correct an error estimate of a prediction. A predefined number of times, an input dataset is split into a training dataset and a validation dataset, a predictive model and a domain model are trained, the trained predictive model and the trained domain model are validated, a predictive error value, a residual value, and a domain error value are computed, and each value is stored in output data. A domain threshold value is computed from the stored domain error values. Each predictive error value and each residual value stored in the output data is stored in in-domain output data when a respective domain error value is less than or equal to the computed domain threshold value. Curve descriptive values are computed to describe a relationship between the residual values as a function of the prediction error values stored in the in-domain output data.