# 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