Supervised Learning

Contents:

• Performance Metrics
  • Basic
  • Advanced
  • Probabilistic
• Area Under ROC Curve (ROC AUC)
• Average Precision (AP)
• Confusion Matrix
• Wilcoxon Signed-Rank Test/Paired t-test
• Bootstrap Confidence Intervals
• Robustness and Generalization
  • Adversarial Testing
  • Noise Robustness
  • Domain Adaption Testing
• Calibration Curves
• Expected Calibration Error (ECE)
• Predication Intervals
• Bias and Fairness Analysis
• Privacy Impact Assessment
• Explainability Requirements
• Benchmarking Against State-of-the-Art
• Ablation Studies
• Extreme Conditions Testing
• Edge Case Scenarios
• Catastrophic Forgetting Tests
• Incremental Learning Performance
• Bayesian Neural Networks Testing
• Monte Carlo Dropout
• Cultural Sensitivity Checks
• Localization Sensitivity