MLS-C01 Modeling Practice Question
A company is using Amazon SageMaker to tune hyperparameters for a gradient boosting model. The objective is to minimize root mean squared error (RMSE). The data scientist wants to explore the hyperparameter space efficiently. Which THREE hyperparameter tuning strategies should the data scientist consider? (Choose 3.)
⚠ Common exam trap
Candidates often assume grid search is the most thorough strategy, but in practice it is inefficient for high-dimensional spaces, while SageMaker explicitly supports Bayesian optimization, random search, and Hyperband as the three built-in tuning strategies.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Bayesian optimization
Bayesian optimization is correct because it builds a probabilistic model of the objective function (RMSE) and uses an acquisition function to select the next hyperparameter combination to evaluate. This approach is sample-efficient, making it ideal for expensive-to-evaluate models like gradient boosting, as it balances exploration and exploitation to find optimal hyperparameters with fewer trials.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Bayesian optimization
Why this is correct
Uses probabilistic model to guide search.
- ✓
Random search
Why this is correct
Samples randomly, can be efficient.
- ✗
Grid search
Why it's wrong here
Exhaustive search, inefficient for large spaces.
- ✗
Manual search
Why it's wrong here
Not automated, inefficient for large spaces.
- ✓
Hyperband
Why this is correct
Early stopping with adaptive resource allocation.
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