Analytical results for uncertainty propagation through trained machine learning regression models

arXiv:2404.11224v1 Announce Type: cross
Abstract: Machine learning (ML) models are increasingly being used in metrology applications. However, for ML models to be credible in a metrology context they should be accompanied by principled uncertainty quantification. This paper addresses the challenge of uncertainty propagation through trained/fixed machine learning (ML) regression models. Analytical expressions for the mean and variance of the model output are obtained/presented for certain input data distributions and for a variety of ML models. Our results cover several popular …

abstract applications arxiv challenge context credible cs.lg however machine machine learning ml models paper propagation quantification regression results stat.ml through type uncertainty