Question 614 of 1,672
MLS-C01 Modeling Practice Question
A machine learning engineer is tuning a Gradient Boosting model for a regression task. The dataset contains 50 features and 100,000 samples. The engineer wants to speed up training without sacrificing predictive performance significantly. Which THREE hyperparameters should the engineer consider adjusting? (Choose THREE.)
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
✓
Reduce the subsample ratio (e.g., from 1.0 to 0.5)
(subsample) uses a fraction of samples per tree, reducing both overfitting and training time. Option D (max_depth) limits tree depth, which directly reduces computation. Option E (max_features) restricts the number of features considered for each split, lowering tree complexity. Option B (learning_rate and n_estimators) trades off; although increasing learning rate can reduce training time, it often requires careful adjustment and may hurt performance. Option C (n_estimators) directly increases training time, which is the opposite of the goal.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce the subsample ratio (e.g., from 1.0 to 0.5)
Why this is correct
Using fewer samples per tree speeds training.
- ✗
Increase learning_rate and decrease n_estimators proportionally
Why it's wrong here
This may not speed up; often needed more estimators.
- ✗
Increase the number of estimators
Why it's wrong here
More estimators slow training.
- ✓
Decrease max_depth of trees
Why this is correct
Shallower trees are faster to build.
- ✓
Reduce max_features (e.g., from 'auto' to 0.5)
Why this is correct
Fewer features per split speeds up training.
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Last reviewed: Jun 20, 2026
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