教你怎么量化机器学习的不确定性，让模型更可靠。
Uncertainty in Machine Learning
- 区分不同类型不确定性，用方法量化预测置信度
- 支持线性回归、随机森林、神经网络等多类模型
- 适合关注模型可靠性与风险决策的从业者
本章介绍机器学习中不确定性量化的基本原理与实际应用。阐述如何识别和区分不同类型的不确定性，并提供针对线性回归、随机森林和神经网络等模型的不确定性量化方法。重点介绍了合规模型（conformal prediction）框架，可生成具有预设置信区间的预测结果。最后探讨了不确定性估计在提升业务决策质量、增强模型可靠性及制定风险敏感策略中的作用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。