In a Spark MLlib project, you are working with a large dataset and need to perform hyperparameter tuning to improve the performance of your machine learning model. Which of the following hyperparameter tuning techniques can be applied in Spark MLlib, and how do they work?

Simulated

Grid search, which exhaustively searches through a predefined grid of hyperparameter values to find the best combination.

0.0%

Random search, which randomly samples hyperparameter values from a predefined search space to find the best combination.

3.9%

Bayesian optimization, which uses a probabilistic model to guide the search for the best hyperparameter values.

6.5%

All of the above, as Spark MLlib supports various hyperparameter tuning techniques.

89.6%

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