Which of the following is a step when using Hyperopt?

When using Hyperopt, specifying the hyperparameter search space is a critical step. Hyperopt is a powerful library for hyperparameter optimization, and the search space defines the range and types of values that Hyperopt will explore to find the best model configuration.

Defining the hyperparameter search space involves specifying the parameters to be tuned along with their respective distributions or ranges. This can include numerical ranges for continuous parameters or sets for categorical parameters. The effectiveness of the tuning process heavily relies on a well-defined search space because it guides Hyperopt in deciding which combinations of hyperparameters to test during optimization.

Other steps in the model training process may involve defining the model architecture and running various forms of search (like grid search), but these are not specific to Hyperopt. Additionally, saving intermediate outputs can be a good practice in long-running experiments, but it is not an inherent step specifically associated with Hyperopt's functionality. The focus in Hyperopt is mainly on efficiently searching the specified space to fine-tune hyperparameters, which is why identifying that search space is essential.