MLA-C01 Question 187
Single answerYou are training a machine learning model on AWS SageMaker, and you want to optimize its hyperparameters to achieve the best performance. Your dataset is large, and training the model is computationally expensive. Which hyperparameter tuning technique is most appropriate in this scenario?
- A
Grid search
- B
Bayesian optimization
- C
Random search
- D
Manual tuning
Show answer and explanation
Correct answer: B
Explanation
Bayesian optimization is the most appropriate technique in this scenario because it intelligently explores the hyperparameter space using probabilistic models, thereby reducing the number of training iterations required. This makes it efficient and effective when training is computationally expensive or datasets are large.
- A. Incorrect.
Grid search evaluates all possible combinations of hyperparameters exhaustively, which is computationally expensive and not suitable for large datasets or expensive training tasks.
- B. Correct.
Bayesian optimization uses probabilistic models to select the next set of hyperparameters to evaluate, making it computationally efficient and well-suited for scenarios with expensive training tasks.
- C. Incorrect.
Random search selects hyperparameters randomly within a defined range, but it does not systematically focus on promising areas of the hyperparameter space, making it less efficient compared to Bayesian optimization in this context.
- D. Incorrect.
Manual tuning requires human intervention and trial-and-error, which is inefficient and impractical for large datasets and expensive training tasks.