Hyperparameter Tuning Quiz

What you'll be asked

1. Which of the following best describes the role of hyperparameters in machine learning?
2. When comparing Grid Search and Random Search for hyperparameter tuning, which statement is true?
3. You are tuning a model and notice that your training loss is decreasing, but your validation loss is increasing significantly. Which hyperparameter adjustment might help mitigate this?
4. In Bayesian Optimization for hyperparameter tuning, what is the purpose of the 'surrogate model'?
5. Why is it considered a best practice to use a separate validation set or cross-validation during hyperparameter tuning?

Answers and explanations come after you submit.