MLS-C01 Modeling Practice Question
A company is using Amazon SageMaker to train an XGBoost model. The training data contains missing values. Which TWO methods can XGBoost handle missing values internally?
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
✓
Learn the best direction to go when a value is missing.
XGBoost can handle missing values internally using a sparsity-aware algorithm. Option C correctly states that XGBoost learns the best direction to go when a value is missing. Option D correctly states that XGBoost treats missing values as a separate category. Option A (surrogate splits) is not used by XGBoost; Option B (dropping rows) is not an internal method; Option E (imputing with mean) is not an internal method.
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 surrogate splits to handle missing values.
Why it's wrong here
Surrogate splits are used in tree algorithms like CART, not default in XGBoost.
- ✗
Drop rows with missing values.
Why it's wrong here
Dropping rows is not an internal XGBoost method.
- ✓
Learn the best direction to go when a value is missing.
Why this is correct
XGBoost uses a sparsity-aware algorithm that learns the optimal split direction for missing values.
- ✓
Treat missing values as a separate category.
Why this is correct
XGBoost can treat missing values as a separate category and learn splits accordingly.
- ✗
Impute missing values with the mean of the feature.
Why it's wrong here
Imputation is not an internal XGBoost method; it requires preprocessing.
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