Bayesian Optimization for Hyperparameters Quiz

Answers and explanations appear as you take the quiz.

1. What does the surrogate model represent in Bayesian optimization?
   • A probabilistic approximation of performance across hyperparameter settings
   • The final trained model's input data
   • A fixed grid of every configuration
   • A causal diagram of feature effects
2. What does an acquisition function help choose?
   • Which examples to delete from the test set
   • Which hyperparameter configuration to evaluate next
   • The final model's class labels
   • The number of training observations
3. How does expected improvement balance exploration and exploitation?
   • It selects the most uncertain point regardless of performance
   • It evaluates only the setting with the best predicted mean
   • It considers expected gain over the current best, including uncertainty
   • It maximizes the training set size
4. Why reserve a final test set during hyperparameter search?
   • The final test score sets the acquisition function
   • The test set improves surrogate smoothness automatically
   • Bayesian optimization cannot use validation data
   • Repeated tuning on test results leaks information into model selection
5. What can a badly chosen search space do?
   • Guarantee the optimum is found
   • Exclude strong configurations or waste trials on irrelevant regions
   • Remove evaluation noise
   • Make validation unnecessary
6. Why repeat noisy optimization with different seeds?
   • It changes categorical settings into continuous ones
   • It makes all trials independent of validation scores
   • The trial path and training noise can change the selected setting
   • It guarantees the best possible score
7. Which role can a Gaussian process surrogate provide?
   • A replacement for the held-out test set
   • Exact performance for every untried configuration
   • A rule that prevents overfitting by itself
   • Predictive mean and uncertainty for candidate settings
8. What should a search report include for reproducibility?
   • Bounds, trial budget, validation design and optimization settings
   • Only the single best score
   • Every test score used during tuning
   • The alphabetical list of parameter names