MLA-C01 Question 184
Single answerYou are training a machine learning model using AWS SageMaker, and you need 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 for 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 most suitable technique for computationally expensive tasks and large datasets because it uses previous evaluations to guide its search, making it more efficient than Grid Search and Random Search. In this scenario, where training is costly, Bayesian Optimization can reduce the number of training runs required to find optimal hyperparameters.
- A. Incorrect.
Grid Search systematically evaluates all possible combinations of hyperparameters. While it guarantees exploration of the full space, it is computationally expensive and inefficient for large datasets or high-dimensional hyperparameter spaces.
- B. Incorrect.
Random Search samples random combinations of hyperparameters and is more efficient than Grid Search for high-dimensional spaces. However, it does not leverage prior knowledge from previous runs, making it less suitable for computationally expensive scenarios.
- C. Correct.
Bayesian Optimization uses prior knowledge from previous iterations to intelligently explore the hyperparameter space, making it highly efficient for computationally expensive tasks or large datasets. This makes it the best choice in this scenario.
- D. Incorrect.
Manual Tuning involves manually setting hyperparameters based on intuition or trial and error. While simple, it is inefficient and not scalable for complex models or large datasets.