Databricks Machine Learning Associate Question 572
Single answerYou are building a machine learning model in Databricks and want to optimize its hyperparameters. Your dataset is very large, and the training process is computationally expensive. Which of the following methods would be the most efficient choice for hyperparameter tuning in this scenario?
- A
Random search
- B
Grid search
- C
Bayesian search
- D
Manual tuning
Show answer and explanation
Correct answer: C
Explanation
Bayesian search is the most efficient hyperparameter tuning method for scenarios involving large datasets and computationally expensive models. It uses a probabilistic approach to balance exploration and exploitation, significantly reducing the number of training iterations compared to random or grid search.
- A. Incorrect.
Random search is more efficient than grid search for hyperparameter tuning, especially for large datasets, but it does not leverage prior evaluations to guide the search process, making it less efficient than Bayesian search.
- B. Incorrect.
Grid search exhaustively searches the entire hyperparameter space, which can be computationally very expensive, making it unsuitable for large datasets or expensive training processes.
- C. Correct.
Bayesian search is the most efficient method here as it uses a probabilistic model to prioritize hyperparameters that are more likely to improve the model, reducing the number of evaluations needed.
- D. Incorrect.
Manual tuning is highly inefficient and impractical for large datasets or models with complex hyperparameter spaces, as it relies on guesswork and trial-and-error.