MLA-C01 Question 185
Single answerYou are training a machine learning model using Amazon SageMaker and want to optimize its performance by tuning hyperparameters. Your dataset is large, and each training job is computationally expensive. Which hyperparameter tuning strategy is most suitable for efficiently identifying the optimal hyperparameters in this scenario?
- A
Grid search
- B
Random search
- C
Bayesian optimization
- D
Manual tuning
Show answer and explanation
Correct answer: C
Explanation
Bayesian optimization is the best choice in this scenario because it is designed to efficiently explore the hyperparameter space by leveraging prior knowledge and results from previous training jobs. This approach is particularly well-suited for computationally expensive jobs, as it reduces the number of training jobs required to find near-optimal hyperparameters, compared to exhaustive methods like grid search or non-intelligent methods like random search.
- A. Incorrect.
Grid search exhaustively evaluates all possible combinations of hyperparameters, which is computationally expensive and inefficient when dealing with a large dataset and expensive training jobs.
- B. Incorrect.
Random search selects hyperparameters randomly, which can sometimes yield good results but is not as efficient as Bayesian optimization for computationally expensive models since it does not leverage prior knowledge.
- C. Correct.
Bayesian optimization uses previous results to intelligently choose the next set of hyperparameters to test, making it a more efficient approach for computationally expensive training jobs.
- D. Incorrect.
Manual tuning requires a significant amount of manual effort, is prone to human error, and is not an efficient or scalable method for identifying optimal hyperparameters in this scenario.