Uncertainty in Machine Learning

Hans Weytjens, Wouter Verbeke
10/7/2025
cs.LG

No official code found. Best implementation: IBM/UQ360 (268 stars, Apache-2.0, last commit 2025-09-17).

Abstract

This book chapter introduces the principles and practical applications of uncertainty quantification in machine learning. It explains how to identify and distinguish between different types of uncertainty and presents methods for quantifying uncertainty in predictive models, including linear regression, random forests, and neural networks. The chapter also covers conformal prediction as a framework for generating predictions with predefined confidence intervals. Finally, it explores how uncertainty estimation can be leveraged to improve business decision-making, enhance model reliability, and support risk-aware strategies.

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Code Implementations(1)

IBM/UQ360Community69%
Apache-2.0

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

26865R, PythonApr 28, 20211 years agoApache-2.0
artificial-intelligencebayesianneuralnetworkcalibrationmachine-learningmetrics+4 more

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Cite this paper

@article{weytjens2025uncertainty,
  title  = {Uncertainty in Machine Learning},
  author = {Hans Weytjens and Wouter Verbeke},
  year   = {2025},
  eprint = {2510.06007},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.06007v1}
}

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