MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a model on a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., postal codes, product IDs). Which feature engineering approach is most suitable for handling these high-cardinality categorical features in a tree-based model?
⚠ Common exam trap
Many candidates default to one-hot encoding for categorical features without considering the model type, failing to recognize that tree-based models can effectively use label encoding for high-cardinality features without the drawbacks of dimensionality explosion.
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 label encoding
Label encoding is suitable for tree-based models because these models split on feature values and can handle ordinal relationships implicitly. Unlike linear models, tree-based models do not assume any distance metric between categories, so label encoding avoids the dimensionality explosion of one-hot encoding while preserving the ability to capture splits based on high-cardinality features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
One-hot encode the categorical features
Why it's wrong here
One-hot encoding high-cardinality features creates a huge number of dummy variables, leading to high memory usage and sparse data.
- ✓
Use label encoding
Why this is correct
Tree-based models like XGBoost can effectively use label-encoded features because they make splits based on ordering.
- ✗
Apply target encoding
Why it's wrong here
Target encoding (mean of target per category) can introduce target leakage and overfitting if not done with cross-validation.
- ✗
Apply binary encoding
Why it's wrong here
Binary encoding is possible but not as directly supported; tree models can work with label encoding natively.
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