MLS-C01 Modeling Practice Question
A machine learning engineer is using SageMaker's built-in XGBoost algorithm for a multi-class classification problem. The training job completes but the model accuracy is low. Which THREE hyperparameters should the engineer tune to improve performance?
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
✓
eta (learning rate)
XGBoost hyperparameters: 'num_round' (number of boosting rounds), 'eta' (learning rate), and 'max_depth' (tree depth) are key for improving accuracy. 'subsample' can help but is less direct. 'min_child_weight' also important but these three are most common. 'colsample_bytree' is for feature subsampling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
eta (learning rate)
Why this is correct
Learning rate controls contribution of each tree; tuning helps convergence.
- ✓
num_round
Why this is correct
Number of boosting rounds; more rounds can improve accuracy but may overfit.
- ✗
subsample
Why it's wrong here
Subsample helps prevent overfitting but may not directly improve accuracy.
- ✓
max_depth
Why this is correct
Depth of trees; deeper trees can capture more complex patterns.
- ✗
colsample_bytree
Why it's wrong here
Feature subsampling can help with overfitting but is not a primary tuning parameter for accuracy.
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