What are the three optional input parameters for SparkTrials?

The correct choice for the three optional input parameters for SparkTrials includes parallelism, timeout, and spark_session.

In the context of executing hyperparameter tuning jobs with SparkTrials, these parameters play a crucial role in optimizing the trials' execution:

• Parallelism allows for the specification of the number of trials to run in parallel. This is important in distributed environments where leveraging multiple nodes can greatly reduce the overall time spent on hyperparameter tuning.

• Timeout sets a limit on how long each trial can run before it is forcibly terminated. This ensures that resources are not wasted on trials that take too long or are unlikely to yield useful results.

• Spark_session refers to the Spark session that will be used for executing the trials. Providing a specific Spark session enables you to maintain control and consistency over the Spark environment in which the trials will run.

These parameters help to fine-tune the behavior of SparkTrials, making it more efficient and aligned with the specific requirements of a particular machine learning task.