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

🎯 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

📝 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)

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

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?

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)

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

🎁 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

📊 Self-Review Guide

📦 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)

💡 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

🔍 Example Structure

# 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

📚 Resources

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

❓ 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).

🎓 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

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