Which SparkTrials parameter is indirectly related to the efficiency of hyperparameter optimization?

The choice related to the efficiency of hyperparameter optimization is indeed parallelism. Parallelism in the context of SparkTrials allows the optimization process to leverage multiple computing resources simultaneously. By distributing the search for optimal hyperparameters across multiple executors or threads, the overall time taken to explore the hyperparameter space is significantly reduced. This leads to faster convergence towards the best-performing model configurations, directly impacting the efficiency of the optimization process.

In hyperparameter tuning, the objective is to find the best combinations of hyperparameters that maximize model performance. When parallelism is effectively utilized, many combinations can be evaluated concurrently, improving resource utilization and enabling faster experiments compared to a sequential approach.

The other options do not have the same indirect impact on efficiency: execution time relates more to the duration of specific operations rather than the optimization process itself, num_features deals with the data dimensionality, and max_iterations refers to the maximum iterations of an algorithm rather than the efficiency of hyperparameter tuning.