Model Performance Validation & Analysis

[levelslip]

• Description: Validate ML model performance and quality
• Activities:
  • Validate baseline model:
    • Evaluate on test set (independent data)
    • Calculate MAE, RMSE, R² metrics
    • Analyze baseline performance
  • Validate advanced model:
    • Evaluate on test set
    • Compare with baseline model
    • Assess if target metrics are met (MAE ≤ 15 min)
  • Test model generalization:
    • Test on different time periods
    • Test on different departments
    • Test on different patient types
  • Identify model weaknesses:
    • Identify scenarios where model underperforms
    • Analyze prediction errors
    • Identify patterns in failures
  • Cross-validation:
    • Perform k-fold cross-validation
    • Assess model stability across folds
    • Identify high-variance scenarios
  • Create performance dashboards:
    • Visualize prediction errors
    • Create performance by category (department, time, etc)
    • Document validation results
• Deliverables: Model validation report, performance dashboards