Hyperparameter Tuning

#Learning Objectives

• Implement grid search, random search, and Bayesian optimization from scratch and compare their sample efficiency
• Explain why random search outperforms grid search when most hyperparameters have low effective dimensionality
• Build a Bayesian optimization loop using a surrogate model and acquisition function to guide the search
• Design a hyperparameter tuning strategy that avoids overfitting the validation set through proper cross-validation