Google Professional Machine Learning Engineer Question 265
Select 3Google Cloud PlatformYou are tasked with building a machine learning model on Google Cloud AI Platform. Your team has decided to use hyperparameter tuning to optimize the model's performance. To ensure the most efficient and effective tuning process, which of the following approaches should you take using Google Cloud AI Platform's Hyperparameter Tuning service?
- A
Use random search for hyperparameter tuning if you have a limited budget and want a quick but less exhaustive search.
- B
Set the
goalfield to maximize or minimize an evaluation metric such as accuracy or loss. - C
Use manual tuning by iteratively training models with different hyperparameters instead of Google Cloud's hyperparameter tuning service.
- D
Configure a wide range for hyperparameters but limit the maximum number of trials to manage costs.
- E
Use a single trial with default hyperparameter values to quickly evaluate the baseline performance.
Show answer and explanation
Correct answers: A, B, D
Explanation
Google Cloud AI Platform's Hyperparameter Tuning service is designed to automate and optimize the process of finding the best hyperparameters for a model. Random search is useful for budget-conscious scenarios, and setting the goal field is essential for directing the optimization process. Configuring reasonable ranges for hyperparameters while controlling the number of trials ensures a balance between comprehensive search and cost management. Manual tuning and using default hyperparameters do not leverage the platform's capabilities effectively.
- A. Correct.
Correct: Random search is a good choice for hyperparameter tuning when you have budget constraints and need quick results, even if it does not explore the entire search space exhaustively.
- B. Correct.
Correct: Setting the
goalfield ensures that the tuning process knows which metric to optimize, making the process efficient and aligned with your objectives. - C. Incorrect.
Incorrect: Manual tuning is inefficient and does not leverage Google Cloud's Hyperparameter Tuning service, which is designed to automate and optimize this process.
- D. Correct.
Correct: Configuring a wide range for hyperparameters ensures exploration of the search space, while limiting the number of trials helps manage costs effectively.
- E. Incorrect.
Incorrect: Using a single trial with default hyperparameter values does not constitute hyperparameter tuning and does not optimize the model's performance.