Databricks Certified Machine Learning - Associate
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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?
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?
Explanation:
In a Spark MLlib project, various hyperparameter tuning techniques can be applied to improve the performance of the machine learning model. Grid search exhaustively searches through a predefined grid of hyperparameter values to find the best combination, ensuring that all possible combinations are evaluated. Random search randomly samples hyperparameter values from a predefined search space to find the best combination, providing a more efficient search compared to grid search. Bayesian optimization uses a probabilistic model to guide the search for the best hyperparameter values, balancing exploration and exploitation in the search process. Spark MLlib supports these hyperparameter tuning techniques, allowing users to choose the appropriate method based on their specific requirements and dataset characteristics.