Hyperparameter Optimization

Hyperparameter optimization is the search for the settings (learning rate, depth, regularization) that a model does not learn on its own, using grid, random, or Bayesian search. Applied AI interviews probe it because the difference between candidates is usually method choice and budget discipline: knowing why random search beats grid in high dimensions, how successive halving spends compute on promising configs, and how to run the search without quietly leaking the test set into your model selection.