What is the hyperparameter optimization method that evaluates multiple parameter combinations?

The hyperparameter optimization method focused on evaluating multiple parameter combinations is grid search. This comprehensive technique systematically explores the specified hyperparameter space by selecting a set of values for each hyperparameter and evaluating all possible combinations of these values.

Grid search is particularly useful when the dimensionality of the hyperparameters is manageable, as it ensures that the best combination is found by exhaustively searching through the predefined grid of parameter values. This approach allows practitioners to see the performance metrics of each combination, providing a clear understanding of how different settings affect the model's performance.

In contrast, while Bayesian optimization and random search are also valid optimization methods, they do not evaluate all combinations. Bayesian optimization builds a model of the objective function and uses it to guide the search for the optimum, being more strategic and potentially efficient but not exhaustive. Random search, on the other hand, samples random combinations, which can be quicker but lacks the systematic thoroughness of grid search. Gradient descent is unrelated to hyperparameter optimization; it is primarily used for optimizing the weights of a model during training by minimizing the loss function.

Grid search is thus the clear choice when the aim is to evaluate all possible combinations of parameters within the defined grids.