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Consider a scenario where you need to perform cross-validation on a machine learning model built using Spark ML. Describe how you would set up a cross-validation process in Spark ML, including the selection of the number of folds, the type of cross-validation, and how you would interpret the results to optimize the model.
A
Use a fixed 5-fold cross-validation with no adjustments; interpret results based on average performance.
B
Set up a customizable cross-validation process using Spark ML's CrossValidator; select the number of folds based on dataset size and variability, and interpret results by analyzing performance metrics across folds to identify optimal hyperparameters.
C
Perform cross-validation once with a single fold; interpret results directly without optimization.
D
Outsource cross-validation to a third-party service; interpret results based on service recommendations.