# Assignment: Complete Model Evaluation Pipeline

## 📋 Overview

Build a comprehensive model evaluation pipeline that compares multiple models across various metrics, tests for statistical significance, and provides deployment recommendations.

**Suggested timing:** End of Phase 16  
**Scope:** Capstone-style build with optional bonus depth  
**Primary deliverables:** Jupyter Notebook + Report

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## 🎯 Objectives

You will:
- Implement end-to-end model evaluation
- Compare 3+ models with cross-validation
- Evaluate fairness and bias
- Perform statistical significance testing
- Make data-driven deployment decisions

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## 📝 Requirements

### Part 1: Dataset Selection

Choose ONE dataset:

**Option A: Classification** (Recommended)
- UCI Adult Income dataset
- Credit approval dataset  
- Customer churn dataset

**Option B: Regression**
- Boston Housing dataset
- California Housing dataset
- Your own regression dataset

**Requirements:**
- [ ] At least 1000 samples
- [ ] Include protected attribute (gender, age, race) for fairness analysis
- [ ] Document data source and description
- [ ] Perform EDA (5+ visualizations)

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### Part 2: Model Training & Comparison

**Train at least 3 different models:**

Classification options:
- Logistic Regression
- Random Forest
- Gradient Boosting
- SVM
- Neural Network

Regression options:
- Linear Regression
- Ridge/Lasso
- Random Forest Regressor
- Gradient Boosting Regressor
- Neural Network

**Requirements:**
- [ ] Use 5-fold or 10-fold cross-validation
- [ ] Track multiple metrics (accuracy, precision, recall, F1, AUC for classification OR MSE, RMSE, MAE, R² for regression)
- [ ] Measure training and prediction time
- [ ] Create comparison table
- [ ] Visualize results (3+ plots)

**Deliverables:**
- Comparison DataFrame with all metrics
- Bar plots comparing models
- Training/prediction time comparison
- Best performing model identification

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### Part 3: Statistical Testing

**Compare your top 2 models statistically:**

- [ ] Paired t-test on cross-validation scores
- [ ] McNemar's test (classification) or correlation test (regression)
- [ ] Calculate confidence intervals
- [ ] Report p-values and interpret
- [ ] Determine if difference is statistically significant

**Deliverables:**
- Statistical test results
- Interpretation write-up (200+ words)
- Decision: Is one model significantly better?

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### Part 4: Fairness & Bias Evaluation

**Analyze bias across protected attribute:**

Classification:
- [ ] Calculate demographic parity difference
- [ ] Calculate demographic parity ratio
- [ ] Calculate equalized odds difference
- [ ] Check 80% rule compliance
- [ ] Performance breakdown by group (confusion matrices)

Regression:
- [ ] Calculate metrics by group (MAE, RMSE, R²)
- [ ] Test for statistical difference in errors
- [ ] Analyze residual distributions by group

**Requirements:**
- [ ] Test for bias in at least 1 protected attribute
- [ ] Create visualizations (2+ plots)
- [ ] Document findings
- [ ] Recommend mitigation if bias detected

**Deliverables:**
- Fairness metrics table
- Group-wise performance comparison
- Bias detection report (300+ words)
- Mitigation recommendations (if applicable)

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### Part 5: Model Selection & Recommendation

**Make final deployment recommendation:**

Consider:
- Performance metrics
- Statistical significance
- Fairness metrics
- Speed/latency
- Interpretability needs
- Resource constraints

**Requirements:**
- [ ] Multi-objective scoring (create weighted composite)
- [ ] Sensitivity analysis (test different weights)
- [ ] Trade-off visualization
- [ ] Final recommendation with justification

**Deliverables:**
- Composite scoring table
- Trade-off plots (accuracy vs speed, accuracy vs fairness)
- Final recommendation report (500+ words)
- Deployment plan

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## 🎁 Optional Extensions

### Optional Extension 1: Advanced Metrics
- Implement LLM-specific metrics (BLEU, ROUGE, BERTScore) if using text data
- Or implement custom domain-specific metrics

### Optional Extension 2: A/B Test Simulation
- Simulate A/B test between top 2 models
- Calculate required sample size
- Perform power analysis
- Create monitoring dashboard mockup

### Optional Extension 3: Bias Mitigation
- Implement 2+ bias mitigation techniques:
  - Reweighting
  - Threshold optimization
  - Fairlearn constraints
- Compare fairness before/after mitigation

### Optional Extension 4: Production Monitoring Plan
- Design monitoring dashboard
- Define alerting thresholds
- Create model card documentation
- Plan A/B test for deployment

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## 📊 Self-Review Guide

| Component | Points | Criteria |
|-----------|--------|----------|
| **Dataset & EDA** | 10 | Appropriate dataset, thorough EDA, clear documentation |
| **Model Comparison** | 25 | 3+ models, proper cross-validation, comprehensive metrics, good visualizations |
| **Statistical Testing** | 20 | Correct tests applied, proper interpretation, clear conclusions |
| **Fairness Analysis** | 25 | Multiple fairness metrics, group analysis, actionable insights |
| **Final Recommendation** | 20 | Well-reasoned decision, multi-objective analysis, clear documentation |
| **Code Quality** | 10 | Clean, documented, reproducible code |
| **Report Quality** | 10 | Clear writing, professional formatting, complete analysis |
| **Bonus** | +20 | Extra challenges completed |
| **TOTAL** | 120 | |

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## 📦 Deliverables

### Files to Prepare:

1. **Jupyter Notebook** (`evaluation_pipeline.ipynb`)
   - All code with outputs
   - Markdown explanations
   - Visualizations embedded

2. **PDF Report** (`evaluation_report.pdf`)
   - Executive summary
   - Methods description
   - Results and findings
   - Final recommendation
   - Appendix with all plots

3. **README** (`README.md`)
   - How to run code
   - Dependencies list
   - Dataset download instructions
   - Expected runtime

### Suggested Project Structure:
```
phase16_assignment_[your_name]/
├── evaluation_pipeline.ipynb
├── evaluation_report.pdf
├── README.md
├── data/
│   └── dataset.csv (if small)
└── figures/
    └── *.png (exported plots)
```

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## 💡 Tips for Success

1. **Start Early**
   - This assignment requires significant analysis
   - Leave time for statistical testing and report writing

2. **Document Everything**
   - Explain your choices
   - Interpret results in context
   - Justify your recommendation

3. **Focus on Insights**
   - Don't just report numbers
   - Explain what they mean
   - Connect to real-world impact

4. **Be Thorough**
   - Check all requirements
   - Include all visualizations
   - Test your code runs end-to-end

5. **Professional Presentation**
   - Clean code with comments
   - Well-formatted plots
   - Polished report

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## 🔍 Example Structure

```python
# 1. Data Loading & EDA
# 2. Data Preprocessing
# 3. Model Training
# 4. Cross-Validation Evaluation
# 5. Statistical Comparison
# 6. Fairness Analysis
# 7. Multi-Objective Selection
# 8. Final Recommendation
```

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## 📚 Resources

- Scikit-learn documentation: https://scikit-learn.org/stable/
- Fairlearn: https://fairlearn.org/
- Statistical testing guide: scipy.stats
- All Phase 16 notebooks

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## ❓ FAQ

**Q: Can I use my own dataset?**  
A: Yes, if it meets the requirements (1000+ samples, has protected attribute).

**Q: What if my data doesn't have protected attributes?**  
A: Use UCI Adult, Credit Approval, or similar public dataset with demographic info.

**Q: How many models minimum?**  
A: 3 different model types required. More is better!

**Q: Can I work in a group?**  
A: Default to individual work so your evaluation reasoning is clearly your own. If you collaborate, document who did what and keep the analysis attributable.

**Q: What if models perform very similarly?**  
A: Great! Discuss why statistical testing is important. Recommend based on other factors (speed, interpretability, cost).

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## 🎓 Learning Outcomes

After completing this assignment, you will be able to:
- ✅ Design comprehensive model evaluation pipelines
- ✅ Select appropriate metrics for different scenarios
- ✅ Perform rigorous statistical comparisons
- ✅ Identify and measure model bias
- ✅ Make data-driven deployment decisions
- ✅ Communicate technical findings effectively

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**Good luck! This is your chance to demonstrate mastery of model evaluation! 🚀**
