Which of the following changes do they need to make to the above code block in order to accomplish the task?

A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.

As a result, they have the following code block:

Which of the following changes do they need to make to the above code block in order to accomplish the task?
A . Change SparkTrials() to Trials()
B . Reduce num_evals to be less than 10
C . Change fmin() to fmax()
D . Remove the trials=trials argument
E . Remove the algo=tpe.suggest argument

View Answer

Answer: A

Explanation:

The SparkTrials() is used to distribute trials of hyperparameter tuning across a Spark cluster. If the environment does not support Spark or if the user prefers not to use distributed computing for this purpose, switching to Trials() would be appropriate. Trials() is the standard class for managing search trials in Hyperopt but does not distribute the computation. If the user is encountering issues with SparkTrials() possibly due to an unsupported configuration or an error in the cluster setup, using Trials() can be a suitable change for running the optimization locally or in a non-distributed manner.

Reference

Hyperopt documentation: http://hyperopt.github.io/hyperopt/