MLS-C01 Modeling Practice Question
A data scientist is tuning hyperparameters for an XGBoost model on a large dataset using Amazon SageMaker. The training job is taking too long, and they want to speed up the tuning process. Which strategy is most effective?
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
✓
Use Bayesian optimization
Bayesian optimization uses results from previous hyperparameter evaluations to choose the next set, reducing the number of training jobs needed to find optimal hyperparameters. This is especially efficient for large datasets where each training job is expensive. Option B (grid search) is exhaustive and slow for many hyperparameters. Option C (random search) is faster but does not learn from past trials. Option D (reducing max depth) may speed up individual jobs but risks underfitting and does not improve the tuning process itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Bayesian optimization
Why this is correct
Bayesian optimization is more efficient.
- ✗
Use grid search with a fine-grained grid
Why it's wrong here
Grid search is exhaustive and slow.
- ✗
Use random search with more iterations
Why it's wrong here
Random search is less efficient than Bayesian optimization.
- ✗
Reduce the max depth of trees
Why it's wrong here
Reducing max depth may affect model accuracy.
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