Google Professional Machine Learning Engineer Question 267
Single answerGoogle Cloud PlatformYou are building a machine learning model on Google Cloud AI Platform to predict customer churn. You decide to use hyperparameter tuning to optimize your model's performance. You want to maximize efficiency while exploring a wide range of hyperparameter combinations. Which hyperparameter tuning strategy should you choose?
- 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 efficient hyperparameter tuning strategy for this scenario because it intelligently selects the next set of hyperparameters to evaluate based on the results of previous evaluations. This allows it to explore a wide range of hyperparameters while minimizing computational costs, making it ideal for optimizing model performance on Google Cloud AI Platform.
- A. Incorrect.
Grid Search systematically evaluates all possible combinations of hyperparameters, which can be computationally expensive and inefficient, especially when dealing with large search spaces.
- B. Incorrect.
Random Search randomly samples hyperparameter combinations, offering some efficiency compared to Grid Search, but it does not strategically explore the search space.
- C. Correct.
Bayesian Optimization uses previous evaluations to intelligently choose the next set of hyperparameters, making it an efficient and effective strategy for exploring a wide range of hyperparameters.
- D. Incorrect.
Manual Tuning is time-consuming, error-prone, and not scalable for exploring a wide range of hyperparameter combinations.