Uncertainty in Machine Learning
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Teaching Uncertainty Quantification in Machine Learning through Use Cases
Uncertainty in machine learning is not generally taught as general knowledge in Machine Learning course curricula. In this paper we propose a short curriculum for a course about uncertainty in machine learning, and compl…
BIG-bench Machine LearningGeneral KnowledgeOut-of-Distribution DetectionUncertainty QuantificationSources of Uncertainty in Supervised Machine Learning -- A Statisticians' View
Supervised machine learning and predictive models have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago. Besides these successes, it becomes clear, that beyon…
MiscellaneousAleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost sy…
BIG-bench Machine LearningUncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty. While the common goal in uncertainty quantification (UQ) …
BIG-bench Machine LearningUncertainty QuantificationHow Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning
Uncertainty quantification (UQ) is essential for assessing the reliability of Earth observation (EO) products. However, the extensive use of machine learning models in EO introduces an additional layer of complexity, as …
BenchmarkingEarth ObservationImage SegmentationScene Classification+2