8. Case Studies

This chapter includes multiple examples of PiML low-code experimentation.

• 8.1. BikeSharing Data
  • 8.1.1. Load and Prepare Data
  • 8.1.2. Train Intepretable Models
  • 8.1.3. Interpretability and Explainability
  • 8.1.4. Model Diagnostics and Outcome Testing
  • 8.1.5. Model Comparison and Benchmarking
• 8.2. CaliforniaHousing Data
  • 8.2.1. Load and Prepare Data
  • 8.2.2. Train Intepretable Models
  • 8.2.3. Interpretability and Explainability
  • 8.2.4. Model Diagnostics and Outcome Testing
  • 8.2.5. Model Comparison and Benchmarking
• 8.3. TaiwanCredit Data
  • 8.3.1. Load and Prepare Data
  • 8.3.2. Train Intepretable Models
  • 8.3.3. Interpretability and Explainability
  • 8.3.4. Model Diagnostics and Outcome Testing
  • 8.3.5. Model Comparison and Benchmarking
• 8.4. Fairness Simulation Study 1
  • 8.4.1. Load and Prepare Data
  • 8.4.2. Train ML Model(s)
  • 8.4.3. Fairness Testing
• 8.5. Fairness Simulation Study 2
  • 8.5.1. Data Description
  • 8.5.2. Load and Prepare data
  • 8.5.3. Train ML Model(s)
  • 8.5.4. Fairness Testing
  • 8.5.5. Fairness Testing Comparison