Methodology

Developing the Decision Tree

Our decision tree was developed through a systematic review of:

  1. Existing uncertainty quantification methods

  2. Practical deployment considerations

  3. Case studies from industry and research

  4. Feedback from practitioners

Key Decision Factors

The decision tree considers the following factors:

1. Project Constraints

  • Computational budget: Available resources for training and inference

  • Latency requirements: Real-time vs. batch processing needs

  • Model architecture: Compatibility with existing models

2. Data Characteristics

  • Dataset size: Amount of available training data

  • Data quality: Presence of noise, outliers, or labeling errors

  • Distribution shift: Expected changes in data distribution

3. Uncertainty Requirements

  • Type of uncertainty: Epistemic, aleatoric, or both

  • Calibration needs: Requirements for well-calibrated predictions

  • Interpretability: Need for explainable uncertainty estimates

Evaluation Criteria

We evaluate UQ methods based on:

  • Accuracy: Predictive performance

  • Calibration: Reliability of uncertainty estimates

  • Computational efficiency: Training and inference costs

  • Ease of implementation: Integration complexity

  • Robustness: Performance under distribution shift

Method Categories

The decision tree guides users toward one of the following method categories:

  1. Single deterministic models: Baseline approach

  2. Ensemble methods: Multiple models for uncertainty estimation

  3. Bayesian approaches: Probabilistic treatment of model parameters

  4. Post-hoc calibration: Adjusting predictions after training

  5. Test-time augmentation: Using data augmentation for uncertainty