Model evaluation measures how well your model performs, while optimization improves that performance. Proper evaluation prevents overconfident assessments, and optimization finds the best model configuration. This section covers validation techniques, metrics for classification and regression tasks, hyperparameter tuning, and methods to address overfitting and underfitting
