Databricks Machine Learning Associate Question 310
Single answerA data scientist is training a machine learning model on a single-node Databricks cluster. They want to efficiently tune the hyperparameters of the model to improve its performance while minimizing computation time. Which of the following approaches should they use?
- A
Manually adjust hyperparameters and retrain the model iteratively
- B
Use Hyperopt to perform parallelized hyperparameter tuning
- C
Set up a multi-node cluster and manually distribute the hyperparameter tuning process
- D
Use Databricks AutoML to automatically train and tune models on a single-node cluster
Show answer and explanation
Correct answer: B
Explanation
Hyperopt is a framework available in Databricks that helps optimize hyperparameters efficiently by leveraging parallelism, even on a single-node cluster. It allows for faster experimentation and better utilization of resources compared to manual tuning or setting up unnecessary infrastructure.
- A. Incorrect.
Manually adjusting hyperparameters is time-consuming and inefficient, especially when automating this process is possible with tools like Hyperopt.
- B. Correct.
Hyperopt is specifically designed for efficient hyperparameter tuning and supports parallelization, making it an ideal choice for tuning single-node models on Databricks.
- C. Incorrect.
Setting up a multi-node cluster for manual hyperparameter tuning is unnecessary in this case since the problem specifies a single-node model. Additionally, manual distribution is inefficient.
- D. Incorrect.
Databricks AutoML is a powerful tool for automated machine learning, but the scenario specifically mentions using a solution for parallel hyperparameter tuning, which is a key feature of Hyperopt.