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Model Evaluation and Validation

  1. Measuring model performance (R², MSE, MAE)
  2. Training and testing data splits
  3. MicroSim: Cross-validation simulation
  4. Understanding overfitting and underfitting
  5. Bias-variance trade-off
  6. Model selection criteria
  7. Performance metrics for different problem types