MLS-C01 XGBoost missing value handling Practice Question
A data scientist is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The dataset contains missing values. What is the default behavior of SageMaker XGBoost regarding missing values?
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
✓
It automatically learns the best direction (left or right) for missing values during training
SageMaker's built-in XGBoost algorithm treats missing values (i.e., NaN, None, or 0 by default) as a separate category. During training, it automatically learns the best direction (left or right) to assign missing values at each split, based on the reduction in loss. This is the default behavior (Option D). Option A is incorrect because XGBoost does not raise an error; it handles missing values internally. Option B is incorrect because XGBoost does not impute with the column mean; that would require data preprocessing. Option C is incorrect because rows with missing values are not removed; they are included in training with the learned direction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It raises an error and stops training
Why it's wrong here
Incorrect. XGBoost does not raise an error; it handles missing values internally by learning the best direction.
- ✗
It imputes missing values with the column mean
Why it's wrong here
Incorrect. XGBoost does not impute missing values with the column mean; missingness is treated as a separate category during training.
- ✗
It removes rows with missing values
Why it's wrong here
Incorrect. Rows with missing values are not removed; they are used with the learned direction for splits.
- ✓
It automatically learns the best direction (left or right) for missing values during training
Why this is correct
Correct. By default, XGBoost learns the best direction (left or right) for missing values at each split, minimizing the loss function.
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