Machine Learning Uncertainty Estimation via Segmented Model Components
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Solution Overview
Problem
Machine learning models, particularly neural networks, struggle to accurately estimate uncertainty in predictions due to internal noise and limited training data, leading to unsuitable outputs for applications like prediction and control, as they tend to underestimate uncertainty for unseen data.
Innovation Solution
A computer-implemented method and system that utilizes two machine learning model components, one trained to generate predictions and another to produce a calibrated uncertainty estimate, with an anomaly detector to detect deviations in operational data, enabling better uncertainty estimation for both aleatoric and epistemic uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained to generate predictions, then prediction capability is improved, but uncertainty estimation accuracy deteriorates
Solution Approach 1:
The machine learning system is segmented into two distinct model components: a first model component trained to generate predictions and a second model component trained to generate uncertainty estimates. This segmentation allows each component to specialize in its respective function, with the prediction model focusing on accuracy and the uncertainty model focusing on reliable confidence assessment, thereby resolving the contradiction between prediction capability and uncertainty estimation accuracy.
Solution Approach 2:
The second machine learning model component acts as an intermediary that processes the prediction output and associated features to generate calibrated uncertainty estimates. This intermediary component bridges the gap between raw predictions and reliable uncertainty quantification, enabling the system to provide both accurate predictions and trustworthy uncertainty measurements without compromising either function.
2Adaptability or versatility
If a machine learning model processes complex internal mappings, then modeling capability is improved, but uncertainty detection capability deteriorates
Solution Approach 1:
The uncertainty detection function is extracted from the complex internal mappings of the prediction model and assigned to a separate second model component. This extraction allows the primary prediction model to maintain its complex nonlinear modeling capabilities while the dedicated uncertainty model focuses specifically on detecting and quantifying uncertainty, making the detection function more measurable and reliable despite the complexity of the overall system.
3Device complexity
If training data is limited, then model simplicity is maintained, but uncertainty estimation reliability deteriorates
Solution Approach 1:
The second model component is trained in advance using the same training data as the first model, performing preliminary learning of uncertainty patterns before actual prediction tasks. This preliminary action allows the uncertainty model to develop robust estimation capabilities from the available training data, improving reliability without requiring additional complex data collection or processing.
Data Source
AI summary
Disclosed is a computer-implemented method for estimating an uncertainty of a prediction generated by a machine learning system, the method including: receiving first data; training a first machine learning model component of a machine learning system with the received first data, the first machine learning model component is trained to generate a prediction; generating an uncertainty estimate of the prediction; training a second machine learning model component of the machine learning system with second data, the second machine learning model component is trained to generate a calibrated uncertainty estimate of the prediction. Also disclosed is a corresponding system.