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Databricks Certified Machine Learning - Associate

Databricks Certified Machine Learning - Associate

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What role does the timeout argument play in SparkTrials, and how does it affect an fmin() call?

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Explanation:

The timeout argument in SparkTrials is crucial for managing the duration of the fmin() call. It sets a maximum time limit for the entire optimization process. If this limit is exceeded, all ongoing trials are stopped, and the best results obtained up to that point are returned. This ensures efficient use of resources and prevents the optimization process from running indefinitely. Incorrect options include specifying the maximum time for individual trial evaluations (A), determining the time between runs (B), or defining the maximum run time for a SparkTrials run (C), none of which are the primary purpose of the timeout argument.

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