Summary of Analytical Results For Uncertainty Propagation Through Trained Machine Learning Regression Models, by Andrew Thompson
Analytical results for uncertainty propagation through trained machine learning regression models
by Andrew Thompson
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of uncertainty propagation through trained/fixed machine learning regression models in metrology applications. It presents analytical expressions for the mean and variance of model output for various input data distributions and ML models, including linear regression, penalised linear regression, kernel ridge regression, Gaussian Processes, support vector machines, and relevance vector machines. The paper also compares these methods with a Monte Carlo approach from a computational efficiency perspective and illustrates them in a metrology application, such as modelling lithium-ion cell state-of-health based on Electrical Impedance Spectroscopy data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machine learning models in science, like measuring things accurately. It helps make sure that these models are correct by showing how to figure out the uncertainty (or range of possible answers) when you use them. The paper explains different ways to do this for many types of machine learning models and shows examples of how it works. |
Keywords
» Artificial intelligence » Linear regression » Machine learning » Regression
