# 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

1. Need UQ? **Yes** - safety-critical application
2. Computational budget? **Moderate**
3. 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

1. Need UQ? **Yes** - safety-critical with long-tail events
2. Computational budget? **Flexible**
3. 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

1. Need UQ? **Yes** - users require confidence estimates
2. Computational budget? **Tight**
3. 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

1. Need UQ? **Yes** - need to handle ambiguous cases
2. Computational budget? **Moderate**
3. 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

1. **Match method to constraints**: Always consider computational budget first
2. **Validate uncertainty estimates**: Use holdout data and OOD samples
3. **Iterate and refine**: Start with simple methods, add complexity as needed
4. **Consider the full system**: Uncertainty is most valuable when integrated into decision-making
5. **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