Introduction

Background

Uncertainty quantification (UQ) in deep learning has become increasingly important as machine learning models are deployed in high-stakes applications such as medical diagnosis, autonomous driving, and financial forecasting.

Motivation

While numerous uncertainty quantification methods exist, practitioners often struggle to determine which approach is most suitable for their specific use case. This paper aims to provide a practical guide for selecting appropriate UQ methods based on project requirements, constraints, and characteristics.

Scope

This work focuses on:

• Epistemic uncertainty: Uncertainty due to lack of knowledge or data

• Aleatoric uncertainty: Uncertainty inherent in the data or process

• Practical considerations: Computational cost, implementation complexity, and interpretability

Target Audience

This guide is designed for:

• Machine learning engineers deploying models in production

• Data scientists working on safety-critical applications

• Researchers seeking to incorporate uncertainty quantification in their work

• Technical managers making decisions about UQ implementation

Organization of This Paper

The remainder of this paper is organized as follows:

• Methodology: Our approach to developing the decision tree

• Decision Tree: The core decision tree framework

• Case Studies: Real-world examples demonstrating the use of the decision tree

• Conclusions: Summary and future directions