Description: The ‘Search Space’ in the context of hyperparameter optimization refers to the set of all possible combinations of hyperparameters that can be evaluated for a machine learning model. Hyperparameters are parameters set before the training process that influence the model’s performance. This space can be multidimensional, and its size can vary significantly depending on the number of hyperparameters and the ranges of values considered. For example, if a model has three hyperparameters, each with three possible values, the search space would contain 27 different combinations. Exploring this space is crucial, as an appropriate selection of hyperparameters can significantly improve the model’s accuracy and generalization. However, exhaustive search in a large search space can be computationally expensive, leading to the development of more efficient techniques, such as random search and Bayesian optimization, which allow for finding optimal combinations without evaluating all possibilities. In summary, the search space is a fundamental concept in hyperparameter optimization, as it defines the scope in which the best configuration for a machine learning model is sought.
