Case Studies
Real-World Applications of the Decision Tree
This section presents case studies demonstrating how to apply the decision tree framework to real-world scenarios.
Case Study 1: Medical Image Classification
Problem Description
A healthcare startup is developing a deep learning model to classify chest X-rays for pneumonia detection. The model will assist radiologists in prioritizing cases for review.
Requirements Analysis
Computational constraints: Moderate - can handle 2-3x inference cost
Uncertainty needs: High - need to flag uncertain cases for human review
Data characteristics: Limited dataset (~5,000 images)
Safety requirements: Critical - misdiagnosis has serious consequences
Decision Path
Need UQ? Yes - safety-critical application
Computational budget? Moderate
Primary uncertainty type? Both epistemic and aleatoric
Recommended Approach
Small ensemble (3-5 models) with temperature scaling
Train 3-5 models with different initializations
Apply temperature scaling for calibration
Flag cases with high disagreement for human review
Outcome
Successfully identified 95% of high-uncertainty cases
Reduced unnecessary human review by 40%
Acceptable computational overhead for clinical workflow
Case Study 2: Autonomous Vehicle Perception
Problem Description
An autonomous vehicle company needs uncertainty estimates for their object detection system to improve safety in edge cases.
Requirements Analysis
Computational constraints: Flexible - safety is paramount
Uncertainty needs: Critical - need epistemic uncertainty for rare events
Data characteristics: Large dataset but long-tail distribution
Latency requirements: Real-time inference (<50ms)
Decision Path
Need UQ? Yes - safety-critical with long-tail events
Computational budget? Flexible
Primary uncertainty type? Epistemic - concern about rare scenarios
Recommended Approach
Deep ensemble with Monte Carlo Dropout
5-model ensemble for robust uncertainty
MC Dropout for additional uncertainty in edge cases
Threshold-based decision making for safe operation
Outcome
Improved detection of out-of-distribution scenarios
30% reduction in false confidence on edge cases
Acceptable inference time with optimized implementation
Case Study 3: Financial Forecasting
Problem Description
A fintech company uses neural networks for stock price prediction and wants to provide confidence intervals to users.
Requirements Analysis
Computational constraints: Tight - high-frequency trading scenario
Uncertainty needs: Moderate - users want confidence intervals
Data characteristics: Large historical dataset
Interpretability: Important for user trust
Decision Path
Need UQ? Yes - users require confidence estimates
Computational budget? Tight
Need quick solution? Yes
Recommended Approach
Post-hoc calibration with quantile regression
Temperature scaling for calibrated probabilities
Quantile regression for prediction intervals
Minimal additional computational cost
Outcome
Provided well-calibrated confidence intervals
Negligible impact on inference latency
Improved user trust and decision-making
Case Study 4: Natural Language Processing
Problem Description
A content moderation platform needs to classify text as safe or unsafe, with uncertainty estimates to route borderline cases for human review.
Requirements Analysis
Computational constraints: Moderate - batch processing acceptable
Uncertainty needs: High - need to identify ambiguous content
Data characteristics: Large but evolving dataset
Distribution shift: Frequent - new types of content emerge
Decision Path
Need UQ? Yes - need to handle ambiguous cases
Computational budget? Moderate
Distribution shift concerns? Yes - important factor
Recommended Approach
Monte Carlo Dropout with test-time augmentation
MC Dropout for epistemic uncertainty
Test-time augmentation for robustness
Human review threshold based on uncertainty
Outcome
85% of ambiguous cases correctly flagged
50% reduction in human review workload
Better handling of emerging content types
Lessons Learned
Key Takeaways
Match method to constraints: Always consider computational budget first
Validate uncertainty estimates: Use holdout data and OOD samples
Iterate and refine: Start with simple methods, add complexity as needed
Consider the full system: Uncertainty is most valuable when integrated into decision-making
Domain expertise matters: Involve domain experts in setting thresholds
Common Success Factors
Clear understanding of uncertainty requirements
Appropriate computational resource allocation
Proper validation of uncertainty estimates
Integration with human decision-making workflows
Continuous monitoring and refinement