Uncertainty in Machine Learning

2025-10-07

AI Summary

This paper addresses the challenge of uncontrolled decision risk arising from insufficient identification and quantification of uncertainty in machine learning models. It systematically distinguishes epistemic uncertainty (model ignorance) from aleatoric uncertainty (inherent data noise) and proposes a unified uncertainty modeling framework grounded in conformal prediction. Methodologically, conformal prediction is integrated into linear regression, random forests, and neural networks to produce prediction intervals with rigorous finite-sample statistical guarantees. A key contribution lies in the decoupled, model-agnostic adaptation of conformal prediction to diverse mainstream models—preserving theoretical validity while ensuring practical deployability. Empirical evaluations demonstrate substantial improvements in predictive reliability and interpretability. The framework supports risk-aware decision-making in domains such as financial risk management and operational optimization, providing a verifiable and reproducible technical pathway for uncertainty-driven business decisions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Quantifying uncertainty in machine learning predictions
• Distinguishing between different types of uncertainty
• Applying uncertainty estimation to improve decision-making

Innovation

• Quantifying uncertainty in predictive models
• Using conformal prediction for confidence intervals
• Leveraging uncertainty to improve decision-making