Databricks Machine Learning Associate Question 315
Single answerYou are working on a distributed machine learning model in Databricks and want to optimize its hyperparameters. You decide to use Bayesian optimization for this task. Which of the following methods would you use to achieve distributed hyperparameter tuning in Databricks?
- A
Hyperopt with SparkTrials
- B
Grid Search with scikit-learn
- C
Random Search with scikit-learn
- D
Manual hyperparameter tuning using a for-loop
Show answer and explanation
Correct answer: A
Explanation
Hyperopt with SparkTrials is the recommended approach in Databricks for distributed hyperparameter tuning using Bayesian optimization. It is designed to efficiently search the hyperparameter space in a distributed environment, making it suitable for large-scale machine learning tasks.
- A. Correct.
Hyperopt with SparkTrials is specifically designed for distributed hyperparameter tuning in Databricks, leveraging Bayesian optimization for efficient search.
- B. Incorrect.
Grid Search with scikit-learn is not optimized for distributed computing and does not use Bayesian optimization. It performs an exhaustive search which can be computationally expensive.
- C. Incorrect.
Random Search with scikit-learn does not use Bayesian optimization and is not inherently designed for distributed tuning in Databricks.
- D. Incorrect.
Manual hyperparameter tuning using a for-loop is inefficient, not scalable, and does not involve Bayesian optimization or distributed computing.